{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CIFAR10 Image Classification using Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import cifar10\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential, load_model, Model\n",
    "from keras.layers import Input, Dense, Dropout, Activation, Flatten\n",
    "from keras.layers import Convolution2D, MaxPooling2D\n",
    "from keras.layers.noise import GaussianNoise\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import time, pickle\n",
    "from keras.utils import to_categorical\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nb_classes = 10\n",
    "class_name = {\n",
    "    0: 'airplane',\n",
    "    1: 'automobile',\n",
    "    2: 'bird',\n",
    "    3: 'cat',\n",
    "    4: 'deer',\n",
    "    5: 'dog',\n",
    "    6: 'frog',\n",
    "    7: 'horse',\n",
    "    8: 'ship',\n",
    "    9: 'truck',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (50000, 32, 32, 3)\n",
      "50000 training samples\n",
      "10000 validation samples\n"
     ]
    }
   ],
   "source": [
    "y_train = y_train.reshape(y_train.shape[0])\n",
    "y_test = y_test.reshape(y_test.shape[0])\n",
    "\n",
    "print('X_train shape:', X_train.shape)\n",
    "print(X_train.shape[0], 'training samples')\n",
    "print(X_test.shape[0], 'validation samples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "X_train /= 255\n",
    "X_test /= 255"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## To Categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_train = to_categorical(y_train, nb_classes)\n",
    "y_test = to_categorical(y_test, nb_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Model # 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 模型采用类似于 `VGG16` 的结构：\n",
    "    - 使用固定尺寸的小卷积核 (3x3)\n",
    "    - 以2的幂次递增的卷积核数量 (64, 128, 256)\n",
    "    - 两层卷积搭配一层池化\n",
    "    - 全连接层没有采用 `VGG16` 庞大的三层结构，避免运算量过大，仅使用 128 个节点的单个FC\n",
    "    - 权重初始化采用He Normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_10 (InputLayer)        (None, 32, 32, 3)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_46 (Conv2D)           (None, 32, 32, 64)        1792      \n",
      "_________________________________________________________________\n",
      "conv2d_47 (Conv2D)           (None, 32, 32, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_23 (MaxPooling (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_48 (Conv2D)           (None, 16, 16, 128)       73856     \n",
      "_________________________________________________________________\n",
      "conv2d_49 (Conv2D)           (None, 16, 16, 128)       147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_24 (MaxPooling (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_50 (Conv2D)           (None, 8, 8, 256)         295168    \n",
      "_________________________________________________________________\n",
      "conv2d_51 (Conv2D)           (None, 8, 8, 256)         590080    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_25 (MaxPooling (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten_9 (Flatten)          (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 128)               524416    \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_18 (Dense)             (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,671,114\n",
      "Trainable params: 1,671,114\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "x = Input(shape=(32, 32, 3))\n",
    "y = x\n",
    "y = Convolution2D(filters=64, kernel_size=3, strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(y)\n",
    "y = Convolution2D(filters=64, kernel_size=3, strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(y)\n",
    "y = MaxPooling2D(pool_size=2, strides=2, padding='valid')(y)\n",
    "\n",
    "y = Convolution2D(filters=128, kernel_size=3, strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(y)\n",
    "y = Convolution2D(filters=128, kernel_size=3, strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(y)\n",
    "y = MaxPooling2D(pool_size=2, strides=2, padding='valid')(y)\n",
    "\n",
    "y = Convolution2D(filters=256, kernel_size=3, strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(y)\n",
    "y = Convolution2D(filters=256, kernel_size=3, strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(y)\n",
    "y = MaxPooling2D(pool_size=2, strides=2, padding='valid')(y)\n",
    "\n",
    "y = Flatten()(y)\n",
    "y = Dense(units=128, activation='relu', kernel_initializer='he_normal')(y)\n",
    "y = Dropout(0.5)(y)\n",
    "y = Dense(units=nb_classes, activation='softmax', kernel_initializer='he_normal')(y)\n",
    "\n",
    "# SGD (Stochastic Gradient Descent)\n",
    "# lrate = 0.01\n",
    "# decay = lrate / nb_epoch\n",
    "# sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)\n",
    "\n",
    "model1 = Model(inputs=x, outputs=y, name='model1')\n",
    "\n",
    "model1.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])\n",
    "\n",
    "model1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "50000/50000 [==============================] - 10s - loss: 2.0132 - acc: 0.2676 - val_loss: 1.5822 - val_acc: 0.4244\n",
      "Epoch 2/100\n",
      "50000/50000 [==============================] - 9s - loss: 1.5418 - acc: 0.4487 - val_loss: 1.2651 - val_acc: 0.5624\n",
      "Epoch 3/100\n",
      "50000/50000 [==============================] - 9s - loss: 1.2879 - acc: 0.5482 - val_loss: 1.1035 - val_acc: 0.6078\n",
      "Epoch 4/100\n",
      "50000/50000 [==============================] - 9s - loss: 1.0868 - acc: 0.6223 - val_loss: 1.0983 - val_acc: 0.6128\n",
      "Epoch 5/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.9453 - acc: 0.6761 - val_loss: 0.9554 - val_acc: 0.6680\n",
      "Epoch 6/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.8175 - acc: 0.7190 - val_loss: 0.8035 - val_acc: 0.7258\n",
      "Epoch 7/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.7194 - acc: 0.7525 - val_loss: 0.9550 - val_acc: 0.6772\n",
      "Epoch 8/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.6354 - acc: 0.7813 - val_loss: 0.7532 - val_acc: 0.7418\n",
      "Epoch 9/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.5519 - acc: 0.8096 - val_loss: 0.7860 - val_acc: 0.7368\n",
      "Epoch 10/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.4827 - acc: 0.8360 - val_loss: 0.7509 - val_acc: 0.7478\n",
      "Epoch 11/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.4134 - acc: 0.8569 - val_loss: 0.8149 - val_acc: 0.7403\n",
      "Epoch 12/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.3556 - acc: 0.8770 - val_loss: 0.7766 - val_acc: 0.7601\n",
      "Epoch 13/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.2991 - acc: 0.8956 - val_loss: 0.8050 - val_acc: 0.7655\n",
      "Epoch 14/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.2478 - acc: 0.9141 - val_loss: 0.8172 - val_acc: 0.7717\n",
      "Epoch 15/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.2044 - acc: 0.9284 - val_loss: 0.8152 - val_acc: 0.7870\n",
      "Epoch 16/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.1750 - acc: 0.9389 - val_loss: 0.9032 - val_acc: 0.7620\n",
      "Epoch 17/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.1450 - acc: 0.9500 - val_loss: 0.9247 - val_acc: 0.7680\n",
      "Epoch 18/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.1253 - acc: 0.9568 - val_loss: 1.0080 - val_acc: 0.7624\n",
      "Epoch 19/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.1085 - acc: 0.9626 - val_loss: 0.9667 - val_acc: 0.7865\n",
      "Epoch 20/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0941 - acc: 0.9677 - val_loss: 1.1030 - val_acc: 0.7678\n",
      "Epoch 21/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0848 - acc: 0.9711 - val_loss: 0.9982 - val_acc: 0.7784\n",
      "Epoch 22/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0759 - acc: 0.9742 - val_loss: 1.0220 - val_acc: 0.7818\n",
      "Epoch 23/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0679 - acc: 0.9778 - val_loss: 1.2585 - val_acc: 0.7805\n",
      "Epoch 24/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0610 - acc: 0.9800 - val_loss: 1.1064 - val_acc: 0.7821\n",
      "Epoch 25/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0572 - acc: 0.9800 - val_loss: 1.3062 - val_acc: 0.7591\n",
      "Epoch 26/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0558 - acc: 0.9810 - val_loss: 1.1978 - val_acc: 0.7861\n",
      "Epoch 27/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0461 - acc: 0.9845 - val_loss: 1.2498 - val_acc: 0.7806\n",
      "Epoch 28/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0481 - acc: 0.9837 - val_loss: 1.2218 - val_acc: 0.7802\n",
      "Epoch 29/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0415 - acc: 0.9858 - val_loss: 1.2239 - val_acc: 0.7855\n",
      "Epoch 30/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0406 - acc: 0.9866 - val_loss: 1.1688 - val_acc: 0.7915\n",
      "Epoch 31/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0361 - acc: 0.9878 - val_loss: 1.3183 - val_acc: 0.7753\n",
      "Epoch 32/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0396 - acc: 0.9868 - val_loss: 1.2737 - val_acc: 0.7861\n",
      "Epoch 33/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0350 - acc: 0.9880 - val_loss: 1.3088 - val_acc: 0.7852\n",
      "Epoch 34/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0306 - acc: 0.9898 - val_loss: 1.4076 - val_acc: 0.7888\n",
      "Epoch 35/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0362 - acc: 0.9873 - val_loss: 1.4883 - val_acc: 0.7662\n",
      "Epoch 36/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0336 - acc: 0.9887 - val_loss: 1.2755 - val_acc: 0.7818\n",
      "Epoch 37/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0283 - acc: 0.9904 - val_loss: 1.3532 - val_acc: 0.7956\n",
      "Epoch 38/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0299 - acc: 0.9903 - val_loss: 1.3491 - val_acc: 0.7943\n",
      "Epoch 39/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0262 - acc: 0.9912 - val_loss: 1.3096 - val_acc: 0.7939\n",
      "Epoch 40/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0283 - acc: 0.9903 - val_loss: 1.3496 - val_acc: 0.7853\n",
      "Epoch 41/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0273 - acc: 0.9916 - val_loss: 1.4623 - val_acc: 0.7790\n",
      "Epoch 42/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0253 - acc: 0.9918 - val_loss: 1.4354 - val_acc: 0.7764\n",
      "Epoch 43/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0264 - acc: 0.9913 - val_loss: 1.3445 - val_acc: 0.7924\n",
      "Epoch 44/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0229 - acc: 0.9923 - val_loss: 1.3986 - val_acc: 0.7916\n",
      "Epoch 45/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0253 - acc: 0.9918 - val_loss: 1.4063 - val_acc: 0.7957\n",
      "Epoch 46/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0236 - acc: 0.9923 - val_loss: 1.3555 - val_acc: 0.7958\n",
      "Epoch 47/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0237 - acc: 0.9923 - val_loss: 1.3931 - val_acc: 0.7911\n",
      "Epoch 48/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0224 - acc: 0.9925 - val_loss: 1.4584 - val_acc: 0.7965\n",
      "Epoch 49/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0220 - acc: 0.9929 - val_loss: 1.4490 - val_acc: 0.7983\n",
      "Epoch 50/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0223 - acc: 0.9928 - val_loss: 1.3526 - val_acc: 0.7959\n",
      "Epoch 51/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0201 - acc: 0.9934 - val_loss: 1.4540 - val_acc: 0.7946\n",
      "Epoch 52/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0210 - acc: 0.9931 - val_loss: 1.5100 - val_acc: 0.7886\n",
      "Epoch 53/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0229 - acc: 0.9928 - val_loss: 1.4590 - val_acc: 0.7948\n",
      "Epoch 54/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0185 - acc: 0.9942 - val_loss: 1.4792 - val_acc: 0.7964\n",
      "Epoch 55/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0184 - acc: 0.9942 - val_loss: 1.4547 - val_acc: 0.7974\n",
      "Epoch 56/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0193 - acc: 0.9940 - val_loss: 1.4772 - val_acc: 0.7810\n",
      "Epoch 57/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0172 - acc: 0.9942 - val_loss: 1.5273 - val_acc: 0.7967\n",
      "Epoch 58/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0175 - acc: 0.9946 - val_loss: 1.5239 - val_acc: 0.7892\n",
      "Epoch 59/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0190 - acc: 0.9941 - val_loss: 1.5165 - val_acc: 0.7906\n",
      "Epoch 60/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0164 - acc: 0.9950 - val_loss: 1.4574 - val_acc: 0.7995\n",
      "Epoch 61/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0168 - acc: 0.9952 - val_loss: 1.4846 - val_acc: 0.8030\n",
      "Epoch 62/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0181 - acc: 0.9944 - val_loss: 1.5014 - val_acc: 0.7844\n",
      "Epoch 63/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0165 - acc: 0.9949 - val_loss: 1.7174 - val_acc: 0.7916\n",
      "Epoch 64/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0174 - acc: 0.9941 - val_loss: 1.6874 - val_acc: 0.7950\n",
      "Epoch 65/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0156 - acc: 0.9955 - val_loss: 1.6053 - val_acc: 0.7839\n",
      "Epoch 66/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0176 - acc: 0.9945 - val_loss: 1.3413 - val_acc: 0.7859\n",
      "Epoch 67/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0151 - acc: 0.9953 - val_loss: 1.6390 - val_acc: 0.7925\n",
      "Epoch 68/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0173 - acc: 0.9946 - val_loss: 1.5625 - val_acc: 0.7979\n",
      "Epoch 69/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0158 - acc: 0.9949 - val_loss: 1.3907 - val_acc: 0.7925\n",
      "Epoch 70/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0172 - acc: 0.9940 - val_loss: 1.4421 - val_acc: 0.7970\n",
      "Epoch 71/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0155 - acc: 0.9951 - val_loss: 1.6257 - val_acc: 0.7958\n",
      "Epoch 72/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0184 - acc: 0.9942 - val_loss: 1.6847 - val_acc: 0.7967\n",
      "Epoch 73/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0129 - acc: 0.9959 - val_loss: 1.5707 - val_acc: 0.7948\n",
      "Epoch 74/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0161 - acc: 0.9952 - val_loss: 1.5351 - val_acc: 0.7871\n",
      "Epoch 75/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0139 - acc: 0.9956 - val_loss: 1.6067 - val_acc: 0.7930\n",
      "Epoch 76/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0137 - acc: 0.9957 - val_loss: 1.6540 - val_acc: 0.7975\n",
      "Epoch 77/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0148 - acc: 0.9954 - val_loss: 1.7616 - val_acc: 0.7953\n",
      "Epoch 78/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0174 - acc: 0.9949 - val_loss: 1.5782 - val_acc: 0.7955\n",
      "Epoch 79/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0164 - acc: 0.9952 - val_loss: 1.5077 - val_acc: 0.7978\n",
      "Epoch 80/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0152 - acc: 0.9955 - val_loss: 1.4543 - val_acc: 0.7956\n",
      "Epoch 81/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0131 - acc: 0.9958 - val_loss: 1.3806 - val_acc: 0.7985\n",
      "Epoch 82/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0152 - acc: 0.9957 - val_loss: 1.5916 - val_acc: 0.8006\n",
      "Epoch 83/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0127 - acc: 0.9962 - val_loss: 1.7823 - val_acc: 0.7962\n",
      "Epoch 84/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0167 - acc: 0.9946 - val_loss: 1.4309 - val_acc: 0.8002\n",
      "Epoch 85/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0146 - acc: 0.9956 - val_loss: 1.5910 - val_acc: 0.8012\n",
      "Epoch 86/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0148 - acc: 0.9956 - val_loss: 1.3991 - val_acc: 0.7895\n",
      "Epoch 87/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0152 - acc: 0.9957 - val_loss: 1.6855 - val_acc: 0.8037\n",
      "Epoch 88/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0142 - acc: 0.9957 - val_loss: 1.8225 - val_acc: 0.7820\n",
      "Epoch 89/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0151 - acc: 0.9954 - val_loss: 1.5438 - val_acc: 0.8012\n",
      "Epoch 90/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0141 - acc: 0.9959 - val_loss: 1.7704 - val_acc: 0.8062\n",
      "Epoch 91/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0150 - acc: 0.9958 - val_loss: 1.5613 - val_acc: 0.7915\n",
      "Epoch 92/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0133 - acc: 0.9965 - val_loss: 1.8280 - val_acc: 0.7933\n",
      "Epoch 93/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0153 - acc: 0.9957 - val_loss: 1.6542 - val_acc: 0.8028\n",
      "Epoch 94/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0146 - acc: 0.9957 - val_loss: 1.4779 - val_acc: 0.7960\n",
      "Epoch 95/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0161 - acc: 0.9956 - val_loss: 1.8158 - val_acc: 0.7971\n",
      "Epoch 96/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0144 - acc: 0.9959 - val_loss: 1.7919 - val_acc: 0.7933\n",
      "Epoch 97/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0130 - acc: 0.9962 - val_loss: 1.7528 - val_acc: 0.7968\n",
      "Epoch 98/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0149 - acc: 0.9955 - val_loss: 1.7122 - val_acc: 0.7999\n",
      "Epoch 99/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0143 - acc: 0.9959 - val_loss: 1.5331 - val_acc: 0.7921\n",
      "Epoch 100/100\n",
      "50000/50000 [==============================] - 9s - loss: 0.0154 - acc: 0.9957 - val_loss: 1.7501 - val_acc: 0.7943\n",
      "@ Total Time Spent: 949.17 seconds\n"
     ]
    }
   ],
   "source": [
    "nb_epoch = 100\n",
    "batch_size = 256\n",
    "start = time.time()\n",
    "h = model1.fit(x=X_train, y=y_train, batch_size=batch_size, epochs=nb_epoch, validation_data=(X_test, y_test), shuffle=True)\n",
    "model1.save('CIFAR10_model_no_data_augmentation.h5')\n",
    "print('@ Total Time Spent: %.2f seconds' % (time.time() - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qwJ+vOV4VNkVRFKXLaEyboijK0EGVtgFCYzDMtX9ZyYZ9ddzz2eM4YdLw/hZJ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c0338z73//+Fo90/jCzG4HPkEzI9Xnn3KdnrLfG+puAIvDnzrmfn8620t6anf64pDfDLe9Yxi3v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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19410c581d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_acc_loss(h, nb_epoch):\n",
    "    acc, loss, val_acc, val_loss = h.history['acc'], h.history['loss'], h.history['val_acc'], h.history['val_loss']\n",
    "    plt.figure(figsize=(15, 5))\n",
    "    plt.subplot(121)\n",
    "    plt.plot(range(nb_epoch), acc, label='Train')\n",
    "    plt.plot(range(nb_epoch), val_acc, label='Test')\n",
    "    plt.title('Accuracy over ' + str(nb_epoch) + ' Epochs', size=15)\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.subplot(122)\n",
    "    plt.plot(range(nb_epoch), loss, label='Train')\n",
    "    plt.plot(range(nb_epoch), val_loss, label='Test')\n",
    "    plt.title('Loss over ' + str(nb_epoch) + ' Epochs', size=15)\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "    \n",
    "plot_acc_loss(h, nb_epoch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可以看到，采用类VGG结构的简化网络可以在100代训练中收敛至准确率80%。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy = 99.92 %     loss = 0.003018\n",
      "Testing Accuracy = 79.43 %    loss = 1.750089\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model1.evaluate(X_train, y_train, verbose=0)\n",
    "print(\"Training Accuracy = %.2f %%     loss = %f\" % (accuracy * 100, loss))\n",
    "loss, accuracy = model1.evaluate(X_test, y_test, verbose=0)\n",
    "print(\"Testing Accuracy = %.2f %%    loss = %f\" % (accuracy * 100, loss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Training with Data Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "195/195 [==============================] - 10s - loss: 0.7602 - acc: 0.7791 - val_loss: 0.6648 - val_acc: 0.8054\n",
      "Epoch 2/100\n",
      "195/195 [==============================] - 10s - loss: 0.5934 - acc: 0.8189 - val_loss: 0.5908 - val_acc: 0.8158\n",
      "Epoch 3/100\n",
      "195/195 [==============================] - 10s - loss: 0.5416 - acc: 0.8287 - val_loss: 0.5641 - val_acc: 0.8196\n",
      "Epoch 4/100\n",
      "195/195 [==============================] - 10s - loss: 0.4983 - acc: 0.8403 - val_loss: 0.5972 - val_acc: 0.8296\n",
      "Epoch 5/100\n",
      "195/195 [==============================] - 10s - loss: 0.4797 - acc: 0.8455 - val_loss: 0.5521 - val_acc: 0.8338\n",
      "Epoch 6/100\n",
      "195/195 [==============================] - 10s - loss: 0.4494 - acc: 0.8555 - val_loss: 0.5502 - val_acc: 0.8295\n",
      "Epoch 7/100\n",
      "195/195 [==============================] - 10s - loss: 0.4265 - acc: 0.8629 - val_loss: 0.5424 - val_acc: 0.8370\n",
      "Epoch 8/100\n",
      "195/195 [==============================] - 10s - loss: 0.4064 - acc: 0.8670 - val_loss: 0.5062 - val_acc: 0.8352\n",
      "Epoch 9/100\n",
      "195/195 [==============================] - 10s - loss: 0.3954 - acc: 0.8694 - val_loss: 0.5378 - val_acc: 0.8371\n",
      "Epoch 10/100\n",
      "195/195 [==============================] - 10s - loss: 0.3776 - acc: 0.8772 - val_loss: 0.5191 - val_acc: 0.8299\n",
      "Epoch 11/100\n",
      "195/195 [==============================] - 10s - loss: 0.3612 - acc: 0.8816 - val_loss: 0.4828 - val_acc: 0.8422\n",
      "Epoch 12/100\n",
      "195/195 [==============================] - 10s - loss: 0.3500 - acc: 0.8862 - val_loss: 0.5007 - val_acc: 0.8468\n",
      "Epoch 13/100\n",
      "195/195 [==============================] - 10s - loss: 0.3369 - acc: 0.8880 - val_loss: 0.4746 - val_acc: 0.8545\n",
      "Epoch 14/100\n",
      "195/195 [==============================] - 10s - loss: 0.3244 - acc: 0.8917 - val_loss: 0.5194 - val_acc: 0.8494\n",
      "Epoch 15/100\n",
      "195/195 [==============================] - 10s - loss: 0.3110 - acc: 0.8966 - val_loss: 0.4860 - val_acc: 0.8560\n",
      "Epoch 16/100\n",
      "195/195 [==============================] - 10s - loss: 0.3066 - acc: 0.8980 - val_loss: 0.4856 - val_acc: 0.8515\n",
      "Epoch 17/100\n",
      "195/195 [==============================] - 10s - loss: 0.2940 - acc: 0.9026 - val_loss: 0.5154 - val_acc: 0.8527\n",
      "Epoch 18/100\n",
      "195/195 [==============================] - 10s - loss: 0.2882 - acc: 0.9043 - val_loss: 0.4846 - val_acc: 0.8455\n",
      "Epoch 19/100\n",
      "195/195 [==============================] - 10s - loss: 0.2740 - acc: 0.9110 - val_loss: 0.4572 - val_acc: 0.8582\n",
      "Epoch 20/100\n",
      "195/195 [==============================] - 10s - loss: 0.2752 - acc: 0.9091 - val_loss: 0.4942 - val_acc: 0.8528\n",
      "Epoch 21/100\n",
      "195/195 [==============================] - 10s - loss: 0.2631 - acc: 0.9126 - val_loss: 0.5639 - val_acc: 0.8498\n",
      "Epoch 22/100\n",
      "195/195 [==============================] - 10s - loss: 0.2534 - acc: 0.9155 - val_loss: 0.4756 - val_acc: 0.8592\n",
      "Epoch 23/100\n",
      "195/195 [==============================] - 10s - loss: 0.2473 - acc: 0.9176 - val_loss: 0.4599 - val_acc: 0.8604\n",
      "Epoch 24/100\n",
      "195/195 [==============================] - 10s - loss: 0.2395 - acc: 0.9212 - val_loss: 0.5436 - val_acc: 0.8627\n",
      "Epoch 25/100\n",
      "195/195 [==============================] - 10s - loss: 0.2309 - acc: 0.9231 - val_loss: 0.4974 - val_acc: 0.8625\n",
      "Epoch 26/100\n",
      "195/195 [==============================] - 10s - loss: 0.2245 - acc: 0.9263 - val_loss: 0.5421 - val_acc: 0.8615\n",
      "Epoch 27/100\n",
      "195/195 [==============================] - 10s - loss: 0.2193 - acc: 0.9277 - val_loss: 0.5264 - val_acc: 0.8660\n",
      "Epoch 28/100\n",
      "195/195 [==============================] - 10s - loss: 0.2158 - acc: 0.9288 - val_loss: 0.5055 - val_acc: 0.8657\n",
      "Epoch 29/100\n",
      "195/195 [==============================] - 10s - loss: 0.2061 - acc: 0.9325 - val_loss: 0.5027 - val_acc: 0.8591\n",
      "Epoch 30/100\n",
      "195/195 [==============================] - 10s - loss: 0.2059 - acc: 0.9315 - val_loss: 0.5014 - val_acc: 0.8600\n",
      "Epoch 31/100\n",
      "195/195 [==============================] - 10s - loss: 0.1991 - acc: 0.9341 - val_loss: 0.5112 - val_acc: 0.8612\n",
      "Epoch 32/100\n",
      "195/195 [==============================] - 10s - loss: 0.1942 - acc: 0.9350 - val_loss: 0.5061 - val_acc: 0.8685\n",
      "Epoch 33/100\n",
      "195/195 [==============================] - 10s - loss: 0.1934 - acc: 0.9363 - val_loss: 0.6104 - val_acc: 0.8656\n",
      "Epoch 34/100\n",
      "195/195 [==============================] - 10s - loss: 0.1866 - acc: 0.9384 - val_loss: 0.5291 - val_acc: 0.8644\n",
      "Epoch 35/100\n",
      "195/195 [==============================] - 10s - loss: 0.1795 - acc: 0.9416 - val_loss: 0.5572 - val_acc: 0.8643\n",
      "Epoch 36/100\n",
      "195/195 [==============================] - 10s - loss: 0.1775 - acc: 0.9409 - val_loss: 0.5590 - val_acc: 0.8639\n",
      "Epoch 37/100\n",
      "195/195 [==============================] - 10s - loss: 0.1750 - acc: 0.9433 - val_loss: 0.5228 - val_acc: 0.8715\n",
      "Epoch 38/100\n",
      "195/195 [==============================] - 10s - loss: 0.1764 - acc: 0.9413 - val_loss: 0.5290 - val_acc: 0.8614\n",
      "Epoch 39/100\n",
      "195/195 [==============================] - 10s - loss: 0.1678 - acc: 0.9458 - val_loss: 0.6437 - val_acc: 0.8650\n",
      "Epoch 40/100\n",
      "195/195 [==============================] - 10s - loss: 0.1667 - acc: 0.9457 - val_loss: 0.5955 - val_acc: 0.8658\n",
      "Epoch 41/100\n",
      "195/195 [==============================] - 10s - loss: 0.1554 - acc: 0.9484 - val_loss: 0.7107 - val_acc: 0.8532\n",
      "Epoch 42/100\n",
      "195/195 [==============================] - 10s - loss: 0.1578 - acc: 0.9478 - val_loss: 0.5714 - val_acc: 0.8689\n",
      "Epoch 43/100\n",
      "195/195 [==============================] - 10s - loss: 0.1575 - acc: 0.9473 - val_loss: 0.6038 - val_acc: 0.8689\n",
      "Epoch 44/100\n",
      "195/195 [==============================] - 10s - loss: 0.1514 - acc: 0.9508 - val_loss: 0.5882 - val_acc: 0.8713\n",
      "Epoch 45/100\n",
      "195/195 [==============================] - 10s - loss: 0.1513 - acc: 0.9500 - val_loss: 0.6348 - val_acc: 0.8664\n",
      "Epoch 46/100\n",
      "195/195 [==============================] - 10s - loss: 0.1422 - acc: 0.9542 - val_loss: 0.6339 - val_acc: 0.8643\n",
      "Epoch 47/100\n",
      "195/195 [==============================] - 10s - loss: 0.1427 - acc: 0.9539 - val_loss: 0.5038 - val_acc: 0.8693\n",
      "Epoch 48/100\n",
      "195/195 [==============================] - 10s - loss: 0.1460 - acc: 0.9528 - val_loss: 0.6001 - val_acc: 0.8657\n",
      "Epoch 49/100\n",
      "195/195 [==============================] - 10s - loss: 0.1461 - acc: 0.9533 - val_loss: 0.5792 - val_acc: 0.8662\n",
      "Epoch 50/100\n",
      "195/195 [==============================] - 10s - loss: 0.1392 - acc: 0.9547 - val_loss: 0.5953 - val_acc: 0.8675\n",
      "Epoch 51/100\n",
      "195/195 [==============================] - 10s - loss: 0.1315 - acc: 0.9568 - val_loss: 0.6369 - val_acc: 0.8618\n",
      "Epoch 52/100\n",
      "195/195 [==============================] - 10s - loss: 0.1349 - acc: 0.9573 - val_loss: 0.6694 - val_acc: 0.8677\n",
      "Epoch 53/100\n",
      "195/195 [==============================] - 10s - loss: 0.1311 - acc: 0.9586 - val_loss: 0.5338 - val_acc: 0.8701\n",
      "Epoch 54/100\n",
      "195/195 [==============================] - 10s - loss: 0.1278 - acc: 0.9589 - val_loss: 0.5742 - val_acc: 0.8633\n",
      "Epoch 55/100\n",
      "195/195 [==============================] - 10s - loss: 0.1247 - acc: 0.9600 - val_loss: 0.6728 - val_acc: 0.8603\n",
      "Epoch 56/100\n",
      "195/195 [==============================] - 10s - loss: 0.1267 - acc: 0.9590 - val_loss: 0.6216 - val_acc: 0.8717\n",
      "Epoch 57/100\n",
      "195/195 [==============================] - 10s - loss: 0.1229 - acc: 0.9605 - val_loss: 0.5622 - val_acc: 0.8606\n",
      "Epoch 58/100\n",
      "195/195 [==============================] - 10s - loss: 0.1191 - acc: 0.9613 - val_loss: 0.6277 - val_acc: 0.8685\n",
      "Epoch 59/100\n",
      "195/195 [==============================] - 10s - loss: 0.1189 - acc: 0.9619 - val_loss: 0.6343 - val_acc: 0.8695\n",
      "Epoch 60/100\n",
      "195/195 [==============================] - 10s - loss: 0.1182 - acc: 0.9621 - val_loss: 0.5622 - val_acc: 0.8754\n",
      "Epoch 61/100\n",
      "195/195 [==============================] - 10s - loss: 0.1148 - acc: 0.9629 - val_loss: 0.6479 - val_acc: 0.8658\n",
      "Epoch 62/100\n",
      "195/195 [==============================] - 10s - loss: 0.1164 - acc: 0.9629 - val_loss: 0.6280 - val_acc: 0.8656\n",
      "Epoch 63/100\n",
      "195/195 [==============================] - 10s - loss: 0.1168 - acc: 0.9619 - val_loss: 0.5681 - val_acc: 0.8725\n",
      "Epoch 64/100\n",
      "195/195 [==============================] - 10s - loss: 0.1132 - acc: 0.9647 - val_loss: 0.7601 - val_acc: 0.8727\n",
      "Epoch 65/100\n",
      "195/195 [==============================] - 10s - loss: 0.1077 - acc: 0.9660 - val_loss: 0.5725 - val_acc: 0.8719\n",
      "Epoch 66/100\n",
      "195/195 [==============================] - 10s - loss: 0.1100 - acc: 0.9648 - val_loss: 0.5714 - val_acc: 0.8750\n",
      "Epoch 67/100\n",
      "195/195 [==============================] - 10s - loss: 0.1106 - acc: 0.9647 - val_loss: 0.5261 - val_acc: 0.8680\n",
      "Epoch 68/100\n",
      "195/195 [==============================] - 10s - loss: 0.1105 - acc: 0.9654 - val_loss: 0.6985 - val_acc: 0.8757\n",
      "Epoch 69/100\n",
      "195/195 [==============================] - 10s - loss: 0.1064 - acc: 0.9669 - val_loss: 0.6973 - val_acc: 0.8658\n",
      "Epoch 70/100\n",
      "195/195 [==============================] - 10s - loss: 0.1029 - acc: 0.9669 - val_loss: 0.6801 - val_acc: 0.8703\n",
      "Epoch 71/100\n",
      "195/195 [==============================] - 10s - loss: 0.1053 - acc: 0.9681 - val_loss: 0.6700 - val_acc: 0.8758\n",
      "Epoch 72/100\n",
      "195/195 [==============================] - 10s - loss: 0.0967 - acc: 0.9695 - val_loss: 0.6007 - val_acc: 0.8756\n",
      "Epoch 73/100\n",
      "195/195 [==============================] - 10s - loss: 0.1044 - acc: 0.9674 - val_loss: 0.6897 - val_acc: 0.8684\n",
      "Epoch 74/100\n",
      "195/195 [==============================] - 10s - loss: 0.0967 - acc: 0.9683 - val_loss: 0.7188 - val_acc: 0.8758\n",
      "Epoch 75/100\n",
      "195/195 [==============================] - 10s - loss: 0.1041 - acc: 0.9682 - val_loss: 0.7450 - val_acc: 0.8690\n",
      "Epoch 76/100\n",
      "195/195 [==============================] - 10s - loss: 0.0993 - acc: 0.9696 - val_loss: 0.7275 - val_acc: 0.8754\n",
      "Epoch 77/100\n",
      "195/195 [==============================] - 10s - loss: 0.0975 - acc: 0.9709 - val_loss: 0.6575 - val_acc: 0.8725\n",
      "Epoch 78/100\n",
      "195/195 [==============================] - 10s - loss: 0.0962 - acc: 0.9704 - val_loss: 0.6955 - val_acc: 0.8739\n",
      "Epoch 79/100\n",
      "195/195 [==============================] - 10s - loss: 0.0943 - acc: 0.9702 - val_loss: 0.7453 - val_acc: 0.8730\n",
      "Epoch 80/100\n",
      "195/195 [==============================] - 10s - loss: 0.0936 - acc: 0.9713 - val_loss: 0.7105 - val_acc: 0.8751\n",
      "Epoch 81/100\n",
      "195/195 [==============================] - 10s - loss: 0.0906 - acc: 0.9718 - val_loss: 0.7453 - val_acc: 0.8725\n",
      "Epoch 82/100\n",
      "195/195 [==============================] - 10s - loss: 0.0920 - acc: 0.9716 - val_loss: 0.8132 - val_acc: 0.8741\n",
      "Epoch 83/100\n",
      "195/195 [==============================] - 10s - loss: 0.0917 - acc: 0.9714 - val_loss: 0.6735 - val_acc: 0.8750\n",
      "Epoch 84/100\n",
      "195/195 [==============================] - 10s - loss: 0.0945 - acc: 0.9710 - val_loss: 0.5376 - val_acc: 0.8780\n",
      "Epoch 85/100\n",
      "195/195 [==============================] - 10s - loss: 0.0915 - acc: 0.9717 - val_loss: 0.6817 - val_acc: 0.8773\n",
      "Epoch 86/100\n",
      "195/195 [==============================] - 10s - loss: 0.0892 - acc: 0.9728 - val_loss: 0.6307 - val_acc: 0.8776\n",
      "Epoch 87/100\n",
      "195/195 [==============================] - 10s - loss: 0.0914 - acc: 0.9726 - val_loss: 0.7511 - val_acc: 0.8768\n",
      "Epoch 88/100\n",
      "195/195 [==============================] - 10s - loss: 0.0905 - acc: 0.9724 - val_loss: 0.5803 - val_acc: 0.8761\n",
      "Epoch 89/100\n",
      "195/195 [==============================] - 10s - loss: 0.0860 - acc: 0.9741 - val_loss: 0.6243 - val_acc: 0.8768\n",
      "Epoch 90/100\n",
      "195/195 [==============================] - 10s - loss: 0.0863 - acc: 0.9744 - val_loss: 0.6785 - val_acc: 0.8784\n",
      "Epoch 91/100\n",
      "195/195 [==============================] - 10s - loss: 0.0855 - acc: 0.9738 - val_loss: 0.7121 - val_acc: 0.8740\n",
      "Epoch 92/100\n",
      "195/195 [==============================] - 10s - loss: 0.0895 - acc: 0.9735 - val_loss: 0.7126 - val_acc: 0.8757\n",
      "Epoch 93/100\n",
      "195/195 [==============================] - 10s - loss: 0.0834 - acc: 0.9750 - val_loss: 0.7235 - val_acc: 0.8775\n",
      "Epoch 94/100\n",
      "195/195 [==============================] - 10s - loss: 0.0900 - acc: 0.9734 - val_loss: 0.6140 - val_acc: 0.8723\n",
      "Epoch 95/100\n",
      "195/195 [==============================] - 10s - loss: 0.0885 - acc: 0.9737 - val_loss: 0.7195 - val_acc: 0.8826\n",
      "Epoch 96/100\n",
      "195/195 [==============================] - 10s - loss: 0.0832 - acc: 0.9746 - val_loss: 0.7164 - val_acc: 0.8806\n",
      "Epoch 97/100\n",
      "195/195 [==============================] - 10s - loss: 0.0852 - acc: 0.9747 - val_loss: 0.7834 - val_acc: 0.8806\n",
      "Epoch 98/100\n",
      "195/195 [==============================] - 10s - loss: 0.0803 - acc: 0.9757 - val_loss: 0.5888 - val_acc: 0.8795\n",
      "Epoch 99/100\n",
      "195/195 [==============================] - 10s - loss: 0.0841 - acc: 0.9751 - val_loss: 0.6971 - val_acc: 0.8788\n",
      "Epoch 100/100\n",
      "195/195 [==============================] - 10s - loss: 0.0835 - acc: 0.9748 - val_loss: 0.6463 - val_acc: 0.8742\n"
     ]
    }
   ],
   "source": [
    "aug_gen = ImageDataGenerator(\n",
    "    featurewise_center = False,  # set input mean to 0 over the dataset\n",
    "    samplewise_center = False,  # set each sample mean to 0\n",
    "    featurewise_std_normalization = False,  # divide inputs by std of the dataset\n",
    "    samplewise_std_normalization = False,  # divide each input by its std\n",
    "    zca_whitening = False,  # apply ZCA whitening\n",
    "    rotation_range = 0,  # randomly rotate images in the range (degrees, 0 to 180)\n",
    "    width_shift_range = 0.1,  # randomly shift images horizontally (fraction of total width)\n",
    "    height_shift_range = 0.1,  # randomly shift images vertically (fraction of total height)\n",
    "    horizontal_flip = True,  # randomly flip images\n",
    "    vertical_flip = False,  # randomly flip images\n",
    ")\n",
    "\n",
    "aug_gen.fit(X_train)\n",
    "gen = aug_gen.flow(X_train, y_train, batch_size=batch_size)\n",
    "h = model1.fit_generator(generator=gen, steps_per_epoch=50000//batch_size, epochs=nb_epoch, validation_data=(X_test, y_test))\n",
    "model1.save('CIFAR10_model_with_data_augmentation.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JaWkpZ511FgAFBQWEh4eTkWFrWK1evZqoqNBSl69YsYILLriAkSNH9pmsitLf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\nnoiIiIiISDvz/wEViDdtBdjuxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19407a35908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acc_loss(h, nb_epoch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可以看到，使用数据增强后继续训练模型，可以将准确率从80%提升至87%。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy = 99.41 %     loss = 0.022593\n",
      "Testing Accuracy = 87.42 %    loss = 0.646288\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model1.evaluate(X_train, y_train, verbose=0)\n",
    "print(\"Training Accuracy = %.2f %%     loss = %f\" % (accuracy * 100, loss))\n",
    "loss, accuracy = model1.evaluate(X_test, y_test, verbose=0)\n",
    "print(\"Testing Accuracy = %.2f %%    loss = %f\" % (accuracy * 100, loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model1.save('CIFAR10_model_with_data_augmentation_0.88.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model1 = load_model('CIFAR10_model_with_data_augmentation_0.88.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Model 2 - Batch Norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_11 (InputLayer)        (None, 32, 32, 3)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_52 (Conv2D)           (None, 32, 32, 64)        1792      \n",
      "_________________________________________________________________\n",
      "batch_normalization_30 (Batc (None, 32, 32, 64)        256       \n",
      "_________________________________________________________________\n",
      "activation_16 (Activation)   (None, 32, 32, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_53 (Conv2D)           (None, 32, 32, 64)        36928     \n",
      "_________________________________________________________________\n",
      "batch_normalization_31 (Batc (None, 32, 32, 64)        256       \n",
      "_________________________________________________________________\n",
      "activation_17 (Activation)   (None, 32, 32, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_26 (MaxPooling (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_54 (Conv2D)           (None, 16, 16, 128)       73856     \n",
      "_________________________________________________________________\n",
      "batch_normalization_32 (Batc (None, 16, 16, 128)       512       \n",
      "_________________________________________________________________\n",
      "activation_18 (Activation)   (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_55 (Conv2D)           (None, 16, 16, 128)       147584    \n",
      "_________________________________________________________________\n",
      "batch_normalization_33 (Batc (None, 16, 16, 128)       512       \n",
      "_________________________________________________________________\n",
      "activation_19 (Activation)   (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_27 (MaxPooling (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_56 (Conv2D)           (None, 8, 8, 256)         295168    \n",
      "_________________________________________________________________\n",
      "batch_normalization_34 (Batc (None, 8, 8, 256)         1024      \n",
      "_________________________________________________________________\n",
      "activation_20 (Activation)   (None, 8, 8, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_57 (Conv2D)           (None, 8, 8, 256)         590080    \n",
      "_________________________________________________________________\n",
      "batch_normalization_35 (Batc (None, 8, 8, 256)         1024      \n",
      "_________________________________________________________________\n",
      "activation_21 (Activation)   (None, 8, 8, 256)         0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_28 (MaxPooling (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten_10 (Flatten)         (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_19 (Dense)             (None, 128)               524416    \n",
      "_________________________________________________________________\n",
      "dropout_10 (Dropout)         (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_20 (Dense)             (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,674,698\n",
      "Trainable params: 1,672,906\n",
      "Non-trainable params: 1,792\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "x = Input(shape=(32, 32, 3))\n",
    "y = x\n",
    "y = Convolution2D(filters=64, kernel_size=3, strides=1, padding='same', activation=None, kernel_initializer='he_normal')(y)\n",
    "y = BatchNormalization()(y)\n",
    "y = Activation('relu')(y)\n",
    "y = Convolution2D(filters=64, kernel_size=3, strides=1, padding='same', activation=None, kernel_initializer='he_normal')(y)\n",
    "y = BatchNormalization()(y)\n",
    "y = Activation('relu')(y)\n",
    "y = MaxPooling2D(pool_size=2, strides=2, padding='valid')(y)\n",
    "\n",
    "y = Convolution2D(filters=128, kernel_size=3, strides=1, padding='same', activation=None, kernel_initializer='he_normal')(y)\n",
    "y = BatchNormalization()(y)\n",
    "y = Activation('relu')(y)\n",
    "y = Convolution2D(filters=128, kernel_size=3, strides=1, padding='same', activation=None, kernel_initializer='he_normal')(y)\n",
    "y = BatchNormalization()(y)\n",
    "y = Activation('relu')(y)\n",
    "y = MaxPooling2D(pool_size=2, strides=2, padding='valid')(y)\n",
    "\n",
    "y = Convolution2D(filters=256, kernel_size=3, strides=1, padding='same', activation=None, kernel_initializer='he_normal')(y)\n",
    "y = BatchNormalization()(y)\n",
    "y = Activation('relu')(y)\n",
    "y = Convolution2D(filters=256, kernel_size=3, strides=1, padding='same', activation=None, kernel_initializer='he_normal')(y)\n",
    "y = BatchNormalization()(y)\n",
    "y = Activation('relu')(y)\n",
    "y = MaxPooling2D(pool_size=2, strides=2, padding='valid')(y)\n",
    "\n",
    "y = Flatten()(y)\n",
    "y = Dense(units=128, activation='relu', kernel_initializer='he_normal')(y)\n",
    "y = Dropout(0.5)(y)\n",
    "y = Dense(units=nb_classes, activation='softmax', kernel_initializer='he_normal')(y)\n",
    "\n",
    "# SGD (Stochastic Gradient Descent)\n",
    "# lrate = 0.01\n",
    "# decay = lrate / nb_epoch\n",
    "# sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)\n",
    "\n",
    "model2 = Model(inputs=x, outputs=y, name='model2')\n",
    "\n",
    "model2.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])\n",
    "\n",
    "model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "50000/50000 [==============================] - 18s - loss: 1.7242 - acc: 0.3890 - val_loss: 2.0180 - val_acc: 0.2580\n",
      "Epoch 2/100\n",
      "50000/50000 [==============================] - 16s - loss: 1.3377 - acc: 0.5282 - val_loss: 1.2614 - val_acc: 0.5571\n",
      "Epoch 3/100\n",
      "50000/50000 [==============================] - 16s - loss: 1.1178 - acc: 0.6119 - val_loss: 1.0913 - val_acc: 0.6062\n",
      "Epoch 4/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.9606 - acc: 0.6681 - val_loss: 1.0528 - val_acc: 0.6515\n",
      "Epoch 5/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.8429 - acc: 0.7119 - val_loss: 1.1813 - val_acc: 0.5988\n",
      "Epoch 6/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.7450 - acc: 0.7428 - val_loss: 1.0895 - val_acc: 0.6511\n",
      "Epoch 7/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.6699 - acc: 0.7699 - val_loss: 0.9586 - val_acc: 0.6894\n",
      "Epoch 8/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.6035 - acc: 0.7911 - val_loss: 0.9109 - val_acc: 0.6922\n",
      "Epoch 9/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.5382 - acc: 0.8144 - val_loss: 0.9238 - val_acc: 0.6929\n",
      "Epoch 10/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.4782 - acc: 0.8346 - val_loss: 1.0419 - val_acc: 0.6841\n",
      "Epoch 11/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.4269 - acc: 0.8521 - val_loss: 0.7982 - val_acc: 0.7368\n",
      "Epoch 12/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.3821 - acc: 0.8679 - val_loss: 0.7552 - val_acc: 0.7685\n",
      "Epoch 13/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.3299 - acc: 0.8860 - val_loss: 1.1928 - val_acc: 0.6813\n",
      "Epoch 14/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.2929 - acc: 0.8987 - val_loss: 0.9217 - val_acc: 0.7305\n",
      "Epoch 15/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.2549 - acc: 0.9124 - val_loss: 1.5567 - val_acc: 0.6533\n",
      "Epoch 16/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.2234 - acc: 0.9224 - val_loss: 0.8005 - val_acc: 0.7796\n",
      "Epoch 17/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.2039 - acc: 0.9291 - val_loss: 1.0152 - val_acc: 0.7314\n",
      "Epoch 18/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.1737 - acc: 0.9404 - val_loss: 0.7752 - val_acc: 0.7977\n",
      "Epoch 19/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.1624 - acc: 0.9438 - val_loss: 0.9552 - val_acc: 0.7656\n",
      "Epoch 20/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.1399 - acc: 0.9531 - val_loss: 1.7900 - val_acc: 0.6679\n",
      "Epoch 21/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.1254 - acc: 0.9571 - val_loss: 0.6992 - val_acc: 0.8127\n",
      "Epoch 22/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.1119 - acc: 0.9609 - val_loss: 1.3947 - val_acc: 0.7218\n",
      "Epoch 23/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.1009 - acc: 0.9665 - val_loss: 0.9049 - val_acc: 0.7828\n",
      "Epoch 24/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0882 - acc: 0.9710 - val_loss: 1.1884 - val_acc: 0.7460\n",
      "Epoch 25/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0888 - acc: 0.9705 - val_loss: 0.9083 - val_acc: 0.7984\n",
      "Epoch 26/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0744 - acc: 0.9758 - val_loss: 1.2040 - val_acc: 0.7774\n",
      "Epoch 27/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0717 - acc: 0.9756 - val_loss: 1.0048 - val_acc: 0.7906\n",
      "Epoch 28/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0697 - acc: 0.9768 - val_loss: 1.4467 - val_acc: 0.7197\n",
      "Epoch 29/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0614 - acc: 0.9794 - val_loss: 1.5246 - val_acc: 0.7401\n",
      "Epoch 30/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0599 - acc: 0.9801 - val_loss: 1.3276 - val_acc: 0.7407\n",
      "Epoch 31/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0502 - acc: 0.9840 - val_loss: 1.1487 - val_acc: 0.7711\n",
      "Epoch 32/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0549 - acc: 0.9817 - val_loss: 0.9999 - val_acc: 0.7902\n",
      "Epoch 33/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0506 - acc: 0.9832 - val_loss: 1.0422 - val_acc: 0.7937\n",
      "Epoch 34/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0478 - acc: 0.9844 - val_loss: 1.0460 - val_acc: 0.8017\n",
      "Epoch 35/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0462 - acc: 0.9852 - val_loss: 0.9323 - val_acc: 0.8099\n",
      "Epoch 36/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0413 - acc: 0.9865 - val_loss: 1.0417 - val_acc: 0.8029\n",
      "Epoch 37/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0389 - acc: 0.9873 - val_loss: 1.0967 - val_acc: 0.8074\n",
      "Epoch 38/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0455 - acc: 0.9852 - val_loss: 1.0775 - val_acc: 0.7954\n",
      "Epoch 39/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0365 - acc: 0.9884 - val_loss: 1.0709 - val_acc: 0.7996\n",
      "Epoch 40/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0371 - acc: 0.9880 - val_loss: 1.3234 - val_acc: 0.7811\n",
      "Epoch 41/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0357 - acc: 0.9888 - val_loss: 1.1615 - val_acc: 0.8041\n",
      "Epoch 42/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0335 - acc: 0.9887 - val_loss: 1.0833 - val_acc: 0.8037\n",
      "Epoch 43/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0326 - acc: 0.9891 - val_loss: 1.1549 - val_acc: 0.8059\n",
      "Epoch 44/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0314 - acc: 0.9896 - val_loss: 1.5865 - val_acc: 0.7468\n",
      "Epoch 45/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0306 - acc: 0.9898 - val_loss: 1.0831 - val_acc: 0.8086\n",
      "Epoch 46/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0301 - acc: 0.9902 - val_loss: 1.2840 - val_acc: 0.7893\n",
      "Epoch 47/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0289 - acc: 0.9906 - val_loss: 1.1920 - val_acc: 0.7976\n",
      "Epoch 48/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0273 - acc: 0.9908 - val_loss: 1.2765 - val_acc: 0.7915\n",
      "Epoch 49/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0274 - acc: 0.9914 - val_loss: 1.1213 - val_acc: 0.8147\n",
      "Epoch 50/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0266 - acc: 0.9911 - val_loss: 1.1202 - val_acc: 0.8126\n",
      "Epoch 51/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0242 - acc: 0.9919 - val_loss: 1.0179 - val_acc: 0.8194\n",
      "Epoch 52/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0245 - acc: 0.9920 - val_loss: 1.4052 - val_acc: 0.7815\n",
      "Epoch 53/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0244 - acc: 0.9923 - val_loss: 1.0932 - val_acc: 0.8140\n",
      "Epoch 54/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0229 - acc: 0.9924 - val_loss: 1.1352 - val_acc: 0.8140\n",
      "Epoch 55/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0228 - acc: 0.9927 - val_loss: 1.3760 - val_acc: 0.7819\n",
      "Epoch 56/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0216 - acc: 0.9931 - val_loss: 1.3707 - val_acc: 0.7864\n",
      "Epoch 57/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0218 - acc: 0.9930 - val_loss: 1.0442 - val_acc: 0.8130\n",
      "Epoch 58/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0245 - acc: 0.9920 - val_loss: 1.0956 - val_acc: 0.8115\n",
      "Epoch 59/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0202 - acc: 0.9940 - val_loss: 1.2647 - val_acc: 0.8065\n",
      "Epoch 60/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0203 - acc: 0.9937 - val_loss: 1.4333 - val_acc: 0.7824\n",
      "Epoch 61/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0213 - acc: 0.9932 - val_loss: 1.3090 - val_acc: 0.7998\n",
      "Epoch 62/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0189 - acc: 0.9936 - val_loss: 1.3135 - val_acc: 0.8043\n",
      "Epoch 63/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0179 - acc: 0.9939 - val_loss: 1.1849 - val_acc: 0.8136\n",
      "Epoch 64/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0193 - acc: 0.9938 - val_loss: 1.1497 - val_acc: 0.8168\n",
      "Epoch 65/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0203 - acc: 0.9937 - val_loss: 1.2381 - val_acc: 0.8050\n",
      "Epoch 66/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0181 - acc: 0.9942 - val_loss: 1.2758 - val_acc: 0.8087\n",
      "Epoch 67/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0170 - acc: 0.9946 - val_loss: 1.5409 - val_acc: 0.7727\n",
      "Epoch 68/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0158 - acc: 0.9947 - val_loss: 1.2648 - val_acc: 0.8044\n",
      "Epoch 69/100\n",
      "50000/50000 [==============================] - 17s - loss: 0.0171 - acc: 0.9946 - val_loss: 1.1720 - val_acc: 0.8164\n",
      "Epoch 70/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0179 - acc: 0.9941 - val_loss: 1.3732 - val_acc: 0.7918\n",
      "Epoch 71/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0168 - acc: 0.9948 - val_loss: 1.1643 - val_acc: 0.8208\n",
      "Epoch 72/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0170 - acc: 0.9942 - val_loss: 1.4766 - val_acc: 0.7813\n",
      "Epoch 73/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0137 - acc: 0.9959 - val_loss: 1.1603 - val_acc: 0.8204\n",
      "Epoch 74/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0166 - acc: 0.9949 - val_loss: 1.0890 - val_acc: 0.8279\n",
      "Epoch 75/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0148 - acc: 0.9953 - val_loss: 1.2181 - val_acc: 0.8159\n",
      "Epoch 76/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0152 - acc: 0.9951 - val_loss: 1.2363 - val_acc: 0.8134\n",
      "Epoch 77/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0165 - acc: 0.9950 - val_loss: 1.2277 - val_acc: 0.8144\n",
      "Epoch 78/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0130 - acc: 0.9960 - val_loss: 1.1696 - val_acc: 0.8192\n",
      "Epoch 79/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0155 - acc: 0.9954 - val_loss: 1.0904 - val_acc: 0.8244\n",
      "Epoch 80/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0146 - acc: 0.9951 - val_loss: 1.4844 - val_acc: 0.7869\n",
      "Epoch 81/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0142 - acc: 0.9954 - val_loss: 1.1822 - val_acc: 0.8177\n",
      "Epoch 82/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0117 - acc: 0.9963 - val_loss: 1.2538 - val_acc: 0.8188\n",
      "Epoch 83/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0126 - acc: 0.9962 - val_loss: 1.2171 - val_acc: 0.8174\n",
      "Epoch 84/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0125 - acc: 0.9959 - val_loss: 1.4067 - val_acc: 0.8013\n",
      "Epoch 85/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0143 - acc: 0.9954 - val_loss: 1.2112 - val_acc: 0.8236\n",
      "Epoch 86/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0138 - acc: 0.9958 - val_loss: 1.2397 - val_acc: 0.8193\n",
      "Epoch 87/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0113 - acc: 0.9960 - val_loss: 1.4343 - val_acc: 0.8122\n",
      "Epoch 88/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0132 - acc: 0.9956 - val_loss: 1.1496 - val_acc: 0.8242\n",
      "Epoch 89/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0139 - acc: 0.9957 - val_loss: 1.3208 - val_acc: 0.8119\n",
      "Epoch 90/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0116 - acc: 0.9960 - val_loss: 1.2577 - val_acc: 0.8207\n",
      "Epoch 91/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0111 - acc: 0.9964 - val_loss: 1.2396 - val_acc: 0.8208\n",
      "Epoch 92/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0143 - acc: 0.9955 - val_loss: 1.3587 - val_acc: 0.8092\n",
      "Epoch 93/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0109 - acc: 0.9964 - val_loss: 1.8233 - val_acc: 0.7701\n",
      "Epoch 94/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0106 - acc: 0.9964 - val_loss: 1.2685 - val_acc: 0.8202\n",
      "Epoch 95/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0125 - acc: 0.9960 - val_loss: 1.6387 - val_acc: 0.7818\n",
      "Epoch 96/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0098 - acc: 0.9971 - val_loss: 1.3609 - val_acc: 0.8142\n",
      "Epoch 97/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0132 - acc: 0.9958 - val_loss: 1.7828 - val_acc: 0.7647\n",
      "Epoch 98/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0103 - acc: 0.9969 - val_loss: 1.2171 - val_acc: 0.8217\n",
      "Epoch 99/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0116 - acc: 0.9960 - val_loss: 1.8173 - val_acc: 0.7761\n",
      "Epoch 100/100\n",
      "50000/50000 [==============================] - 16s - loss: 0.0094 - acc: 0.9969 - val_loss: 1.4868 - val_acc: 0.8079\n",
      "@ Total Time Spent: 1647.86 seconds\n"
     ]
    }
   ],
   "source": [
    "nb_epoch = 100\n",
    "batch_size = 256\n",
    "start = time.time()\n",
    "h = model2.fit(x=X_train, y=y_train, batch_size=batch_size, epochs=nb_epoch, validation_data=(X_test, y_test), shuffle=True)\n",
    "print('@ Total Time Spent: %.2f seconds' % (time.time() - start))\n",
    "model2.save('CIFAR10_model_no_data_augmentation_BN.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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O0YCvFNz67FUURenoqGjromzcV8R/Fu3kUEklKQlxpMTHsSOvhDlr9pEaH8dP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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1941260fbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acc_loss(h, nb_epoch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 加入BN应该能够使网络的收敛速度有所提高，但对于提升模型绝对性能而言效果不大。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy = 98.75 %     loss = 0.070851\n",
      "Testing Accuracy = 80.79 %    loss = 1.486847\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model2.evaluate(X_train, y_train, verbose=0)\n",
    "print(\"Training Accuracy = %.2f %%     loss = %f\" % (accuracy * 100, loss))\n",
    "loss, accuracy = model2.evaluate(X_test, y_test, verbose=0)\n",
    "print(\"Testing Accuracy = %.2f %%    loss = %f\" % (accuracy * 100, loss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Model #3 - Dual GPU Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.layers.merge import Concatenate\n",
    "from keras.layers.core import Lambda\n",
    "from keras.models import Model\n",
    "import tensorflow as tf\n",
    "\n",
    "def make_parallel(model, gpu_count):\n",
    "\n",
    "    def get_slice(data, idx, parts):\n",
    "        shape = tf.shape(data)\n",
    "        size = tf.concat([shape[:1] // parts, shape[1:]], axis=0)\n",
    "        stride = tf.concat([shape[:1] // parts, shape[1:] * 0], axis=0)\n",
    "        start = stride * idx\n",
    "        return tf.slice(data, start, size)\n",
    "\n",
    "    outputs_all = []\n",
    "    for i in range(len(model.outputs)):\n",
    "        outputs_all.append([])\n",
    "\n",
    "    # Place a copy of the model on each GPU, each getting a slice of the batch\n",
    "    for i in range(gpu_count):\n",
    "        with tf.device('/gpu:%d' % i):\n",
    "            with tf.name_scope('tower_%d' % i) as scope:\n",
    "\n",
    "                inputs = []\n",
    "                # Slice each input into a piece for processing on this GPU\n",
    "                for x in model.inputs:\n",
    "                    input_shape = tuple(x.get_shape().as_list())[1:]\n",
    "                    slice_n = Lambda(get_slice, output_shape=input_shape, arguments={'idx': i, 'parts': gpu_count})(x)\n",
    "                    inputs.append(slice_n)\n",
    "\n",
    "                outputs = model(inputs)\n",
    "\n",
    "                if not isinstance(outputs, list):\n",
    "                    outputs = [outputs]\n",
    "\n",
    "                # Save all the outputs for merging back together later\n",
    "                for l in range(len(outputs)):\n",
    "                    outputs_all[l].append(outputs[l])\n",
    "\n",
    "    # merge outputs on CPU\n",
    "    # 也可以设为GPU，如果CPU负载已经很大的话\n",
    "    with tf.device('/cpu:0'):\n",
    "        merged = []\n",
    "        for outputs in outputs_all:\n",
    "            merged.append(Concatenate(axis=0)(outputs))\n",
    "        return Model(model.inputs, merged)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "input_17 (InputLayer)            (None, 32, 32, 3)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "lambda_3 (Lambda)                (None, 32, 32, 3)     0           input_17[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "lambda_4 (Lambda)                (None, 32, 32, 3)     0           input_17[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "model1 (Model)                   (None, 10)            1671114     lambda_3[0][0]                   \n",
      "                                                                   lambda_4[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)      (None, 10)            0           model1[1][0]                     \n",
      "                                                                   model1[2][0]                     \n",
      "====================================================================================================\n",
      "Total params: 1,671,114\n",
      "Trainable params: 1,671,114\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "x = Input(shape=(32, 32, 3))\n",
    "y = x\n",
    "y = Convolution2D(filters=64, kernel_size=3, strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(y)\n",
    "y = Convolution2D(filters=64, kernel_size=3, strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(y)\n",
    "y = MaxPooling2D(pool_size=2, strides=2, padding='valid')(y)\n",
    "\n",
    "y = Convolution2D(filters=128, kernel_size=3, strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(y)\n",
    "y = Convolution2D(filters=128, kernel_size=3, strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(y)\n",
    "y = MaxPooling2D(pool_size=2, strides=2, padding='valid')(y)\n",
    "\n",
    "y = Convolution2D(filters=256, kernel_size=3, strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(y)\n",
    "y = Convolution2D(filters=256, kernel_size=3, strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(y)\n",
    "y = MaxPooling2D(pool_size=2, strides=2, padding='valid')(y)\n",
    "\n",
    "y = Flatten()(y)\n",
    "y = Dense(units=128, activation='relu', kernel_initializer='he_normal')(y)\n",
    "y = Dropout(0.5)(y)\n",
    "y = Dense(units=nb_classes, activation='softmax', kernel_initializer='he_normal')(y)\n",
    "\n",
    "# SGD (Stochastic Gradient Descent)\n",
    "# lrate = 0.01\n",
    "# decay = lrate / nb_epoch\n",
    "# sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)\n",
    "\n",
    "model3 = Model(inputs=x, outputs=y, name='model1')\n",
    "\n",
    "model3 = make_parallel(model3, 2)\n",
    "\n",
    "model3.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])\n",
    "\n",
    "model3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "195/195 [==============================] - 13s - loss: 2.0169 - acc: 0.2599 - val_loss: 1.6660 - val_acc: 0.4055\n",
      "Epoch 2/100\n",
      "195/195 [==============================] - 11s - loss: 1.6283 - acc: 0.4108 - val_loss: 1.3916 - val_acc: 0.4973\n",
      "Epoch 3/100\n",
      "195/195 [==============================] - 11s - loss: 1.4173 - acc: 0.4938 - val_loss: 1.1105 - val_acc: 0.6125\n",
      "Epoch 4/100\n",
      "195/195 [==============================] - 10s - loss: 1.2385 - acc: 0.5653 - val_loss: 1.0590 - val_acc: 0.6269\n",
      "Epoch 5/100\n",
      "195/195 [==============================] - 11s - loss: 1.0924 - acc: 0.6214 - val_loss: 0.9224 - val_acc: 0.6753\n",
      "Epoch 6/100\n",
      "195/195 [==============================] - 10s - loss: 0.9861 - acc: 0.6623 - val_loss: 0.7903 - val_acc: 0.7265\n",
      "Epoch 7/100\n",
      "195/195 [==============================] - 10s - loss: 0.8996 - acc: 0.6911 - val_loss: 0.7734 - val_acc: 0.7313\n",
      "Epoch 8/100\n",
      "195/195 [==============================] - 10s - loss: 0.8280 - acc: 0.7152 - val_loss: 0.6874 - val_acc: 0.7618\n",
      "Epoch 9/100\n",
      "195/195 [==============================] - 10s - loss: 0.7703 - acc: 0.7360 - val_loss: 0.6880 - val_acc: 0.7635\n",
      "Epoch 10/100\n",
      "195/195 [==============================] - 10s - loss: 0.7220 - acc: 0.7552 - val_loss: 0.6134 - val_acc: 0.7901\n",
      "Epoch 11/100\n",
      "195/195 [==============================] - 11s - loss: 0.6720 - acc: 0.7710 - val_loss: 0.5991 - val_acc: 0.7955\n",
      "Epoch 12/100\n",
      "195/195 [==============================] - 11s - loss: 0.6397 - acc: 0.7834 - val_loss: 0.5442 - val_acc: 0.8134\n",
      "Epoch 13/100\n",
      "195/195 [==============================] - 11s - loss: 0.6051 - acc: 0.7958 - val_loss: 0.6128 - val_acc: 0.7927\n",
      "Epoch 14/100\n",
      "195/195 [==============================] - 10s - loss: 0.5720 - acc: 0.8060 - val_loss: 0.5306 - val_acc: 0.8186\n",
      "Epoch 15/100\n",
      "195/195 [==============================] - 10s - loss: 0.5485 - acc: 0.8147 - val_loss: 0.5086 - val_acc: 0.8266\n",
      "Epoch 16/100\n",
      "195/195 [==============================] - 10s - loss: 0.5208 - acc: 0.8251 - val_loss: 0.4921 - val_acc: 0.8366\n",
      "Epoch 17/100\n",
      "195/195 [==============================] - 10s - loss: 0.4977 - acc: 0.8321 - val_loss: 0.5340 - val_acc: 0.8159\n",
      "Epoch 18/100\n",
      "195/195 [==============================] - 10s - loss: 0.4758 - acc: 0.8378 - val_loss: 0.4886 - val_acc: 0.8361\n",
      "Epoch 19/100\n",
      "195/195 [==============================] - 11s - loss: 0.4572 - acc: 0.8452 - val_loss: 0.5174 - val_acc: 0.8273\n",
      "Epoch 20/100\n",
      "195/195 [==============================] - 10s - loss: 0.4334 - acc: 0.8537 - val_loss: 0.5143 - val_acc: 0.8303\n",
      "Epoch 21/100\n",
      "195/195 [==============================] - 10s - loss: 0.4216 - acc: 0.8555 - val_loss: 0.4948 - val_acc: 0.8407\n",
      "Epoch 22/100\n",
      "195/195 [==============================] - 10s - loss: 0.3981 - acc: 0.8644 - val_loss: 0.4757 - val_acc: 0.8403\n",
      "Epoch 23/100\n",
      "195/195 [==============================] - 10s - loss: 0.3871 - acc: 0.8677 - val_loss: 0.4450 - val_acc: 0.8510\n",
      "Epoch 24/100\n",
      "195/195 [==============================] - 10s - loss: 0.3709 - acc: 0.8724 - val_loss: 0.4580 - val_acc: 0.8453\n",
      "Epoch 25/100\n",
      "195/195 [==============================] - 10s - loss: 0.3534 - acc: 0.8814 - val_loss: 0.4447 - val_acc: 0.8537\n",
      "Epoch 26/100\n",
      "195/195 [==============================] - 10s - loss: 0.3423 - acc: 0.8819 - val_loss: 0.4460 - val_acc: 0.8538\n",
      "Epoch 27/100\n",
      "195/195 [==============================] - 10s - loss: 0.3274 - acc: 0.8897 - val_loss: 0.4280 - val_acc: 0.8605\n",
      "Epoch 28/100\n",
      "195/195 [==============================] - 10s - loss: 0.3174 - acc: 0.8917 - val_loss: 0.4370 - val_acc: 0.8630\n",
      "Epoch 29/100\n",
      "195/195 [==============================] - 10s - loss: 0.3027 - acc: 0.8977 - val_loss: 0.4280 - val_acc: 0.8627\n",
      "Epoch 30/100\n",
      "195/195 [==============================] - 10s - loss: 0.2954 - acc: 0.8995 - val_loss: 0.4225 - val_acc: 0.8655\n",
      "Epoch 31/100\n",
      "195/195 [==============================] - 10s - loss: 0.2751 - acc: 0.9065 - val_loss: 0.4342 - val_acc: 0.8565\n",
      "Epoch 32/100\n",
      "195/195 [==============================] - 10s - loss: 0.2736 - acc: 0.9063 - val_loss: 0.4305 - val_acc: 0.8645\n",
      "Epoch 33/100\n",
      "195/195 [==============================] - 10s - loss: 0.2598 - acc: 0.9103 - val_loss: 0.4340 - val_acc: 0.8704\n",
      "Epoch 34/100\n",
      "195/195 [==============================] - 10s - loss: 0.2530 - acc: 0.9143 - val_loss: 0.4317 - val_acc: 0.8650\n",
      "Epoch 35/100\n",
      "195/195 [==============================] - 10s - loss: 0.2438 - acc: 0.9164 - val_loss: 0.4220 - val_acc: 0.8668\n",
      "Epoch 36/100\n",
      "195/195 [==============================] - 10s - loss: 0.2366 - acc: 0.9196 - val_loss: 0.4466 - val_acc: 0.8605\n",
      "Epoch 37/100\n",
      "195/195 [==============================] - 11s - loss: 0.2282 - acc: 0.9214 - val_loss: 0.4534 - val_acc: 0.8655\n",
      "Epoch 38/100\n",
      "195/195 [==============================] - 10s - loss: 0.2247 - acc: 0.9238 - val_loss: 0.4229 - val_acc: 0.8692\n",
      "Epoch 39/100\n",
      "195/195 [==============================] - 10s - loss: 0.2131 - acc: 0.9263 - val_loss: 0.4553 - val_acc: 0.8651\n",
      "Epoch 40/100\n",
      "195/195 [==============================] - 10s - loss: 0.2061 - acc: 0.9293 - val_loss: 0.4273 - val_acc: 0.8741\n",
      "Epoch 41/100\n",
      "195/195 [==============================] - 11s - loss: 0.1969 - acc: 0.9330 - val_loss: 0.4574 - val_acc: 0.8672\n",
      "Epoch 42/100\n",
      "195/195 [==============================] - 10s - loss: 0.1928 - acc: 0.9338 - val_loss: 0.4582 - val_acc: 0.8680\n",
      "Epoch 43/100\n",
      "195/195 [==============================] - 10s - loss: 0.1880 - acc: 0.9353 - val_loss: 0.4309 - val_acc: 0.8723\n",
      "Epoch 44/100\n",
      "195/195 [==============================] - 10s - loss: 0.1866 - acc: 0.9359 - val_loss: 0.4446 - val_acc: 0.8714\n",
      "Epoch 45/100\n",
      "195/195 [==============================] - 10s - loss: 0.1801 - acc: 0.9383 - val_loss: 0.4638 - val_acc: 0.8757\n",
      "Epoch 46/100\n",
      "195/195 [==============================] - 10s - loss: 0.1720 - acc: 0.9421 - val_loss: 0.4559 - val_acc: 0.8735\n",
      "Epoch 47/100\n",
      "195/195 [==============================] - 10s - loss: 0.1679 - acc: 0.9414 - val_loss: 0.4467 - val_acc: 0.8772\n",
      "Epoch 48/100\n",
      "195/195 [==============================] - 10s - loss: 0.1681 - acc: 0.9427 - val_loss: 0.4606 - val_acc: 0.8697\n",
      "Epoch 49/100\n",
      "195/195 [==============================] - 10s - loss: 0.1567 - acc: 0.9460 - val_loss: 0.4885 - val_acc: 0.8729\n",
      "Epoch 50/100\n",
      "195/195 [==============================] - 10s - loss: 0.1509 - acc: 0.9489 - val_loss: 0.4440 - val_acc: 0.8668\n",
      "Epoch 51/100\n",
      "195/195 [==============================] - 10s - loss: 0.1516 - acc: 0.9476 - val_loss: 0.4443 - val_acc: 0.8768\n",
      "Epoch 52/100\n",
      "195/195 [==============================] - 10s - loss: 0.1423 - acc: 0.9523 - val_loss: 0.4689 - val_acc: 0.8790\n",
      "Epoch 53/100\n",
      "195/195 [==============================] - 10s - loss: 0.1398 - acc: 0.9517 - val_loss: 0.4864 - val_acc: 0.8785\n",
      "Epoch 54/100\n",
      "195/195 [==============================] - 10s - loss: 0.1369 - acc: 0.9531 - val_loss: 0.4743 - val_acc: 0.8804\n",
      "Epoch 55/100\n",
      "195/195 [==============================] - 10s - loss: 0.1425 - acc: 0.9523 - val_loss: 0.4766 - val_acc: 0.8685\n",
      "Epoch 56/100\n",
      "195/195 [==============================] - 10s - loss: 0.1285 - acc: 0.9573 - val_loss: 0.4995 - val_acc: 0.8770\n",
      "Epoch 57/100\n",
      "195/195 [==============================] - 10s - loss: 0.1307 - acc: 0.9561 - val_loss: 0.4928 - val_acc: 0.8753\n",
      "Epoch 58/100\n",
      "195/195 [==============================] - 10s - loss: 0.1209 - acc: 0.9592 - val_loss: 0.4841 - val_acc: 0.8786\n",
      "Epoch 59/100\n",
      "195/195 [==============================] - 10s - loss: 0.1260 - acc: 0.9567 - val_loss: 0.4956 - val_acc: 0.8770\n",
      "Epoch 60/100\n",
      "195/195 [==============================] - 10s - loss: 0.1191 - acc: 0.9589 - val_loss: 0.5442 - val_acc: 0.8754\n",
      "Epoch 61/100\n",
      "195/195 [==============================] - 10s - loss: 0.1192 - acc: 0.9599 - val_loss: 0.5654 - val_acc: 0.8757\n",
      "Epoch 62/100\n",
      "195/195 [==============================] - 10s - loss: 0.1200 - acc: 0.9600 - val_loss: 0.5095 - val_acc: 0.8728\n",
      "Epoch 63/100\n",
      "195/195 [==============================] - 11s - loss: 0.1118 - acc: 0.9626 - val_loss: 0.5461 - val_acc: 0.8776\n",
      "Epoch 64/100\n",
      "195/195 [==============================] - 10s - loss: 0.1120 - acc: 0.9633 - val_loss: 0.4964 - val_acc: 0.8762\n",
      "Epoch 65/100\n",
      "195/195 [==============================] - 10s - loss: 0.1090 - acc: 0.9640 - val_loss: 0.5443 - val_acc: 0.8761\n",
      "Epoch 66/100\n",
      "195/195 [==============================] - 10s - loss: 0.1061 - acc: 0.9650 - val_loss: 0.4692 - val_acc: 0.8838\n",
      "Epoch 67/100\n",
      "195/195 [==============================] - 10s - loss: 0.1071 - acc: 0.9645 - val_loss: 0.4734 - val_acc: 0.8801\n",
      "Epoch 68/100\n",
      "195/195 [==============================] - 10s - loss: 0.1041 - acc: 0.9656 - val_loss: 0.5349 - val_acc: 0.8777\n",
      "Epoch 69/100\n",
      "195/195 [==============================] - 10s - loss: 0.1008 - acc: 0.9657 - val_loss: 0.5514 - val_acc: 0.8787\n",
      "Epoch 70/100\n",
      "195/195 [==============================] - 10s - loss: 0.0941 - acc: 0.9696 - val_loss: 0.5088 - val_acc: 0.8819\n",
      "Epoch 71/100\n",
      "195/195 [==============================] - 10s - loss: 0.0953 - acc: 0.9673 - val_loss: 0.5095 - val_acc: 0.8813\n",
      "Epoch 72/100\n",
      "195/195 [==============================] - 11s - loss: 0.0969 - acc: 0.9681 - val_loss: 0.5386 - val_acc: 0.8838\n",
      "Epoch 73/100\n",
      "195/195 [==============================] - 10s - loss: 0.0983 - acc: 0.9679 - val_loss: 0.5173 - val_acc: 0.8832\n",
      "Epoch 74/100\n",
      "195/195 [==============================] - 10s - loss: 0.0929 - acc: 0.9695 - val_loss: 0.5561 - val_acc: 0.8782\n",
      "Epoch 75/100\n",
      "195/195 [==============================] - 11s - loss: 0.0917 - acc: 0.9707 - val_loss: 0.5478 - val_acc: 0.8818\n",
      "Epoch 76/100\n",
      "195/195 [==============================] - 11s - loss: 0.0865 - acc: 0.9713 - val_loss: 0.5340 - val_acc: 0.8834\n",
      "Epoch 77/100\n",
      "195/195 [==============================] - 11s - loss: 0.0905 - acc: 0.9706 - val_loss: 0.5937 - val_acc: 0.8857\n",
      "Epoch 78/100\n",
      "195/195 [==============================] - 10s - loss: 0.0887 - acc: 0.9712 - val_loss: 0.4610 - val_acc: 0.8804\n",
      "Epoch 79/100\n",
      "195/195 [==============================] - 11s - loss: 0.0842 - acc: 0.9728 - val_loss: 0.5864 - val_acc: 0.8759\n",
      "Epoch 80/100\n",
      "195/195 [==============================] - 10s - loss: 0.0821 - acc: 0.9736 - val_loss: 0.5959 - val_acc: 0.8843\n",
      "Epoch 81/100\n",
      "195/195 [==============================] - 11s - loss: 0.0875 - acc: 0.9722 - val_loss: 0.6075 - val_acc: 0.8847\n",
      "Epoch 82/100\n",
      "195/195 [==============================] - 11s - loss: 0.0877 - acc: 0.9715 - val_loss: 0.5504 - val_acc: 0.8841\n",
      "Epoch 83/100\n",
      "195/195 [==============================] - 10s - loss: 0.0814 - acc: 0.9740 - val_loss: 0.6404 - val_acc: 0.8837\n",
      "Epoch 84/100\n",
      "195/195 [==============================] - 10s - loss: 0.0853 - acc: 0.9725 - val_loss: 0.6414 - val_acc: 0.8808\n",
      "Epoch 85/100\n",
      "195/195 [==============================] - 10s - loss: 0.0800 - acc: 0.9742 - val_loss: 0.5737 - val_acc: 0.8831\n",
      "Epoch 86/100\n",
      "195/195 [==============================] - 10s - loss: 0.0833 - acc: 0.9743 - val_loss: 0.5375 - val_acc: 0.8810\n",
      "Epoch 87/100\n",
      "195/195 [==============================] - 10s - loss: 0.0804 - acc: 0.9738 - val_loss: 0.5494 - val_acc: 0.8850\n",
      "Epoch 88/100\n",
      "195/195 [==============================] - 11s - loss: 0.0770 - acc: 0.9747 - val_loss: 0.5339 - val_acc: 0.8820\n",
      "Epoch 89/100\n",
      "195/195 [==============================] - 10s - loss: 0.0809 - acc: 0.9751 - val_loss: 0.6269 - val_acc: 0.8890\n",
      "Epoch 90/100\n",
      "195/195 [==============================] - 10s - loss: 0.0776 - acc: 0.9751 - val_loss: 0.6999 - val_acc: 0.8822\n",
      "Epoch 91/100\n",
      "195/195 [==============================] - 10s - loss: 0.0797 - acc: 0.9754 - val_loss: 0.6705 - val_acc: 0.8819\n",
      "Epoch 92/100\n",
      "195/195 [==============================] - 10s - loss: 0.0786 - acc: 0.9750 - val_loss: 0.6291 - val_acc: 0.8860\n",
      "Epoch 93/100\n",
      "195/195 [==============================] - 10s - loss: 0.0769 - acc: 0.9758 - val_loss: 0.6731 - val_acc: 0.8853\n",
      "Epoch 94/100\n",
      "195/195 [==============================] - 10s - loss: 0.0757 - acc: 0.9768 - val_loss: 0.6710 - val_acc: 0.8891\n",
      "Epoch 95/100\n",
      "195/195 [==============================] - 10s - loss: 0.0788 - acc: 0.9761 - val_loss: 0.5957 - val_acc: 0.8778\n",
      "Epoch 96/100\n",
      "195/195 [==============================] - 10s - loss: 0.0733 - acc: 0.9772 - val_loss: 0.5535 - val_acc: 0.8844\n",
      "Epoch 97/100\n",
      "195/195 [==============================] - 10s - loss: 0.0727 - acc: 0.9768 - val_loss: 0.6136 - val_acc: 0.8850\n",
      "Epoch 98/100\n",
      "195/195 [==============================] - 10s - loss: 0.0720 - acc: 0.9783 - val_loss: 0.5705 - val_acc: 0.8864\n",
      "Epoch 99/100\n",
      "195/195 [==============================] - 10s - loss: 0.0718 - acc: 0.9777 - val_loss: 0.4961 - val_acc: 0.8829\n",
      "Epoch 100/100\n",
      "195/195 [==============================] - 11s - loss: 0.0749 - acc: 0.9766 - val_loss: 0.5694 - val_acc: 0.8900\n"
     ]
    }
   ],
   "source": [
    "aug_gen = ImageDataGenerator(\n",
    "    featurewise_center = False,  # set input mean to 0 over the dataset\n",
    "    samplewise_center = False,  # set each sample mean to 0\n",
    "    featurewise_std_normalization = False,  # divide inputs by std of the dataset\n",
    "    samplewise_std_normalization = False,  # divide each input by its std\n",
    "    zca_whitening = False,  # apply ZCA whitening\n",
    "    rotation_range = 0,  # randomly rotate images in the range (degrees, 0 to 180)\n",
    "    width_shift_range = 0.1,  # randomly shift images horizontally (fraction of total width)\n",
    "    height_shift_range = 0.1,  # randomly shift images vertically (fraction of total height)\n",
    "    horizontal_flip = True,  # randomly flip images\n",
    "    vertical_flip = False,  # randomly flip images\n",
    ")\n",
    "\n",
    "aug_gen.fit(X_train)\n",
    "gen = aug_gen.flow(X_train, y_train, batch_size=batch_size)\n",
    "h = model3.fit_generator(generator=gen, steps_per_epoch=50000//batch_size, epochs=nb_epoch, validation_data=(X_test, y_test))\n",
    "model3.save('CIFAR10_model_with_data_augmentation_dual_GPU.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YmBhNXYOP4so6quoaSIiO4KYzxnHVyZmHuj+1REQYM8DLmAG9Y5CvUqr7JMXY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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1941c05a470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acc_loss(h, nb_epoch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可以看到，使用双卡可以在100代内将未训练的模型准确率直接提升至89%。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy = 99.45 %     loss = 0.018501\n",
      "Testing Accuracy = 89.00 %    loss = 0.569403\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model3.evaluate(X_train, y_train, verbose=0)\n",
    "print(\"Training Accuracy = %.2f %%     loss = %f\" % (accuracy * 100, loss))\n",
    "loss, accuracy = model3.evaluate(X_test, y_test, verbose=0)\n",
    "print(\"Testing Accuracy = %.2f %%    loss = %f\" % (accuracy * 100, loss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Continue Training for another 100 Epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "195/195 [==============================] - 11s - loss: 0.0706 - acc: 0.9783 - val_loss: 0.6915 - val_acc: 0.8870\n",
      "Epoch 2/100\n",
      "195/195 [==============================] - 10s - loss: 0.0685 - acc: 0.9778 - val_loss: 0.6965 - val_acc: 0.8823\n",
      "Epoch 3/100\n",
      "195/195 [==============================] - 10s - loss: 0.0736 - acc: 0.9774 - val_loss: 0.6382 - val_acc: 0.8901\n",
      "Epoch 4/100\n",
      "195/195 [==============================] - 10s - loss: 0.0725 - acc: 0.9778 - val_loss: 0.5873 - val_acc: 0.8870\n",
      "Epoch 5/100\n",
      "195/195 [==============================] - 10s - loss: 0.0715 - acc: 0.9777 - val_loss: 0.5816 - val_acc: 0.8905\n",
      "Epoch 6/100\n",
      "195/195 [==============================] - 10s - loss: 0.0682 - acc: 0.9794 - val_loss: 0.6123 - val_acc: 0.8860\n",
      "Epoch 7/100\n",
      "195/195 [==============================] - 10s - loss: 0.0722 - acc: 0.9776 - val_loss: 0.6525 - val_acc: 0.8808\n",
      "Epoch 8/100\n",
      "195/195 [==============================] - 10s - loss: 0.0688 - acc: 0.9784 - val_loss: 0.6851 - val_acc: 0.8850\n",
      "Epoch 9/100\n",
      "195/195 [==============================] - 10s - loss: 0.0678 - acc: 0.9788 - val_loss: 0.6334 - val_acc: 0.8844\n",
      "Epoch 10/100\n",
      "195/195 [==============================] - 10s - loss: 0.0714 - acc: 0.9783 - val_loss: 0.6708 - val_acc: 0.8816\n",
      "Epoch 11/100\n",
      "195/195 [==============================] - 10s - loss: 0.0669 - acc: 0.9793 - val_loss: 0.6556 - val_acc: 0.8825\n",
      "Epoch 12/100\n",
      "195/195 [==============================] - 10s - loss: 0.0689 - acc: 0.9794 - val_loss: 0.7122 - val_acc: 0.8888\n",
      "Epoch 13/100\n",
      "195/195 [==============================] - 10s - loss: 0.0704 - acc: 0.9782 - val_loss: 0.7068 - val_acc: 0.8847\n",
      "Epoch 14/100\n",
      "195/195 [==============================] - 10s - loss: 0.0622 - acc: 0.9809 - val_loss: 0.7109 - val_acc: 0.8848\n",
      "Epoch 15/100\n",
      "195/195 [==============================] - 10s - loss: 0.0698 - acc: 0.9792 - val_loss: 0.7261 - val_acc: 0.8936\n",
      "Epoch 16/100\n",
      "195/195 [==============================] - 10s - loss: 0.0637 - acc: 0.9811 - val_loss: 0.7378 - val_acc: 0.8880\n",
      "Epoch 17/100\n",
      "195/195 [==============================] - 10s - loss: 0.0640 - acc: 0.9801 - val_loss: 0.7277 - val_acc: 0.8703\n",
      "Epoch 18/100\n",
      "195/195 [==============================] - 10s - loss: 0.0673 - acc: 0.9795 - val_loss: 0.5863 - val_acc: 0.8812\n",
      "Epoch 19/100\n",
      "195/195 [==============================] - 10s - loss: 0.0659 - acc: 0.9812 - val_loss: 0.7484 - val_acc: 0.8862\n",
      "Epoch 20/100\n",
      "195/195 [==============================] - 10s - loss: 0.0642 - acc: 0.9806 - val_loss: 0.6420 - val_acc: 0.8835\n",
      "Epoch 21/100\n",
      "195/195 [==============================] - 10s - loss: 0.0658 - acc: 0.9806 - val_loss: 0.8284 - val_acc: 0.8782\n",
      "Epoch 22/100\n",
      "195/195 [==============================] - 10s - loss: 0.0642 - acc: 0.9805 - val_loss: 0.7191 - val_acc: 0.8897\n",
      "Epoch 23/100\n",
      "195/195 [==============================] - 10s - loss: 0.0674 - acc: 0.9809 - val_loss: 0.6454 - val_acc: 0.8855\n",
      "Epoch 24/100\n",
      "195/195 [==============================] - 10s - loss: 0.0612 - acc: 0.9823 - val_loss: 0.6664 - val_acc: 0.8867\n",
      "Epoch 25/100\n",
      "195/195 [==============================] - 10s - loss: 0.0660 - acc: 0.9804 - val_loss: 0.5884 - val_acc: 0.8859\n",
      "Epoch 26/100\n",
      "195/195 [==============================] - 10s - loss: 0.0652 - acc: 0.9816 - val_loss: 0.8168 - val_acc: 0.8701\n",
      "Epoch 27/100\n",
      "195/195 [==============================] - 10s - loss: 0.0678 - acc: 0.9798 - val_loss: 0.7310 - val_acc: 0.8890\n",
      "Epoch 28/100\n",
      "195/195 [==============================] - 10s - loss: 0.0625 - acc: 0.9816 - val_loss: 0.6809 - val_acc: 0.8843\n",
      "Epoch 29/100\n",
      "195/195 [==============================] - 10s - loss: 0.0645 - acc: 0.9812 - val_loss: 0.6913 - val_acc: 0.8718\n",
      "Epoch 30/100\n",
      "195/195 [==============================] - 10s - loss: 0.0648 - acc: 0.9812 - val_loss: 0.6700 - val_acc: 0.8879\n",
      "Epoch 31/100\n",
      "195/195 [==============================] - 10s - loss: 0.0664 - acc: 0.9812 - val_loss: 0.7105 - val_acc: 0.8875\n",
      "Epoch 32/100\n",
      "195/195 [==============================] - 10s - loss: 0.0650 - acc: 0.9812 - val_loss: 0.6824 - val_acc: 0.8884\n",
      "Epoch 33/100\n",
      "195/195 [==============================] - 10s - loss: 0.0596 - acc: 0.9833 - val_loss: 0.7684 - val_acc: 0.8823\n",
      "Epoch 34/100\n",
      "195/195 [==============================] - 10s - loss: 0.0609 - acc: 0.9825 - val_loss: 0.7830 - val_acc: 0.8860\n",
      "Epoch 35/100\n",
      "195/195 [==============================] - 10s - loss: 0.0706 - acc: 0.9804 - val_loss: 0.7077 - val_acc: 0.8843\n",
      "Epoch 36/100\n",
      "195/195 [==============================] - 10s - loss: 0.0625 - acc: 0.9817 - val_loss: 0.7988 - val_acc: 0.8855\n",
      "Epoch 37/100\n",
      "195/195 [==============================] - 10s - loss: 0.0650 - acc: 0.9817 - val_loss: 0.7748 - val_acc: 0.8814\n",
      "Epoch 38/100\n",
      "195/195 [==============================] - 10s - loss: 0.0656 - acc: 0.9820 - val_loss: 0.6613 - val_acc: 0.8882\n",
      "Epoch 39/100\n",
      "195/195 [==============================] - 10s - loss: 0.0621 - acc: 0.9817 - val_loss: 0.7815 - val_acc: 0.8840\n",
      "Epoch 40/100\n",
      "195/195 [==============================] - 10s - loss: 0.0600 - acc: 0.9826 - val_loss: 0.7607 - val_acc: 0.8882\n",
      "Epoch 41/100\n",
      "195/195 [==============================] - 10s - loss: 0.0634 - acc: 0.9823 - val_loss: 0.6981 - val_acc: 0.8889\n",
      "Epoch 42/100\n",
      "195/195 [==============================] - 10s - loss: 0.0652 - acc: 0.9821 - val_loss: 0.7302 - val_acc: 0.8882\n",
      "Epoch 43/100\n",
      "195/195 [==============================] - 10s - loss: 0.0660 - acc: 0.9818 - val_loss: 0.8005 - val_acc: 0.8880\n",
      "Epoch 44/100\n",
      "195/195 [==============================] - 10s - loss: 0.0607 - acc: 0.9828 - val_loss: 0.9155 - val_acc: 0.8846\n",
      "Epoch 45/100\n",
      "195/195 [==============================] - 10s - loss: 0.0640 - acc: 0.9824 - val_loss: 0.7784 - val_acc: 0.8877\n",
      "Epoch 46/100\n",
      "195/195 [==============================] - 10s - loss: 0.0640 - acc: 0.9827 - val_loss: 0.7362 - val_acc: 0.8864\n",
      "Epoch 47/100\n",
      "195/195 [==============================] - 10s - loss: 0.0645 - acc: 0.9827 - val_loss: 0.7999 - val_acc: 0.8895\n",
      "Epoch 48/100\n",
      "195/195 [==============================] - 10s - loss: 0.0688 - acc: 0.9801 - val_loss: 0.6573 - val_acc: 0.8860\n",
      "Epoch 49/100\n",
      "195/195 [==============================] - 10s - loss: 0.0633 - acc: 0.9824 - val_loss: 0.7105 - val_acc: 0.8860\n",
      "Epoch 50/100\n",
      "195/195 [==============================] - 10s - loss: 0.0638 - acc: 0.9821 - val_loss: 0.6661 - val_acc: 0.8820\n",
      "Epoch 51/100\n",
      "195/195 [==============================] - 10s - loss: 0.0582 - acc: 0.9835 - val_loss: 0.6530 - val_acc: 0.8869\n",
      "Epoch 52/100\n",
      "195/195 [==============================] - 10s - loss: 0.0611 - acc: 0.9828 - val_loss: 0.7573 - val_acc: 0.8894\n",
      "Epoch 53/100\n",
      "195/195 [==============================] - 10s - loss: 0.0597 - acc: 0.9826 - val_loss: 0.7643 - val_acc: 0.8896\n",
      "Epoch 54/100\n",
      "195/195 [==============================] - 10s - loss: 0.0665 - acc: 0.9820 - val_loss: 0.7081 - val_acc: 0.8844\n",
      "Epoch 55/100\n",
      "195/195 [==============================] - 10s - loss: 0.0671 - acc: 0.9825 - val_loss: 0.8069 - val_acc: 0.8878\n",
      "Epoch 56/100\n",
      "195/195 [==============================] - 10s - loss: 0.0672 - acc: 0.9816 - val_loss: 0.5163 - val_acc: 0.8845\n",
      "Epoch 57/100\n",
      "195/195 [==============================] - 10s - loss: 0.0660 - acc: 0.9820 - val_loss: 0.8208 - val_acc: 0.8874\n",
      "Epoch 58/100\n",
      "195/195 [==============================] - 10s - loss: 0.0620 - acc: 0.9834 - val_loss: 0.8083 - val_acc: 0.8943\n",
      "Epoch 59/100\n",
      "195/195 [==============================] - 11s - loss: 0.0636 - acc: 0.9834 - val_loss: 0.6493 - val_acc: 0.8873\n",
      "Epoch 60/100\n",
      "195/195 [==============================] - 10s - loss: 0.0667 - acc: 0.9829 - val_loss: 0.7842 - val_acc: 0.8917\n",
      "Epoch 61/100\n",
      "195/195 [==============================] - 11s - loss: 0.0628 - acc: 0.9829 - val_loss: 0.7673 - val_acc: 0.8886\n",
      "Epoch 62/100\n",
      "195/195 [==============================] - 10s - loss: 0.0651 - acc: 0.9825 - val_loss: 0.8020 - val_acc: 0.8811\n",
      "Epoch 63/100\n",
      "195/195 [==============================] - 10s - loss: 0.0674 - acc: 0.9811 - val_loss: 0.7808 - val_acc: 0.8814\n",
      "Epoch 64/100\n",
      "195/195 [==============================] - 10s - loss: 0.0612 - acc: 0.9833 - val_loss: 0.8362 - val_acc: 0.8877\n",
      "Epoch 65/100\n",
      "195/195 [==============================] - 11s - loss: 0.0666 - acc: 0.9817 - val_loss: 0.7637 - val_acc: 0.8895\n",
      "Epoch 66/100\n",
      "195/195 [==============================] - 11s - loss: 0.0631 - acc: 0.9829 - val_loss: 0.7960 - val_acc: 0.8885\n",
      "Epoch 67/100\n",
      "195/195 [==============================] - 10s - loss: 0.0626 - acc: 0.9829 - val_loss: 0.7306 - val_acc: 0.8894\n",
      "Epoch 68/100\n",
      "195/195 [==============================] - 10s - loss: 0.0623 - acc: 0.9829 - val_loss: 0.5802 - val_acc: 0.8839\n",
      "Epoch 69/100\n",
      "195/195 [==============================] - 10s - loss: 0.0598 - acc: 0.9843 - val_loss: 0.8404 - val_acc: 0.8861\n",
      "Epoch 70/100\n",
      "195/195 [==============================] - 10s - loss: 0.0686 - acc: 0.9816 - val_loss: 0.5963 - val_acc: 0.8900\n",
      "Epoch 71/100\n",
      "195/195 [==============================] - 11s - loss: 0.0689 - acc: 0.9821 - val_loss: 0.7134 - val_acc: 0.8883\n",
      "Epoch 72/100\n",
      "195/195 [==============================] - 10s - loss: 0.0694 - acc: 0.9824 - val_loss: 0.8275 - val_acc: 0.8827\n",
      "Epoch 73/100\n",
      "195/195 [==============================] - 11s - loss: 0.0625 - acc: 0.9832 - val_loss: 0.8607 - val_acc: 0.8841\n",
      "Epoch 74/100\n",
      "195/195 [==============================] - 10s - loss: 0.0693 - acc: 0.9814 - val_loss: 0.7435 - val_acc: 0.8879\n",
      "Epoch 75/100\n",
      "195/195 [==============================] - 10s - loss: 0.0642 - acc: 0.9827 - val_loss: 0.7942 - val_acc: 0.8860\n",
      "Epoch 76/100\n",
      "195/195 [==============================] - 10s - loss: 0.0627 - acc: 0.9829 - val_loss: 0.7997 - val_acc: 0.8857\n",
      "Epoch 77/100\n",
      "195/195 [==============================] - 10s - loss: 0.0685 - acc: 0.9820 - val_loss: 0.7837 - val_acc: 0.8899\n",
      "Epoch 78/100\n",
      "195/195 [==============================] - 10s - loss: 0.0683 - acc: 0.9821 - val_loss: 0.6698 - val_acc: 0.8818\n",
      "Epoch 79/100\n",
      "195/195 [==============================] - 10s - loss: 0.0635 - acc: 0.9834 - val_loss: 0.7850 - val_acc: 0.8881\n",
      "Epoch 80/100\n",
      "195/195 [==============================] - 10s - loss: 0.0646 - acc: 0.9830 - val_loss: 0.8187 - val_acc: 0.8888\n",
      "Epoch 81/100\n",
      "195/195 [==============================] - 10s - loss: 0.0641 - acc: 0.9835 - val_loss: 0.6308 - val_acc: 0.8866\n",
      "Epoch 82/100\n",
      "195/195 [==============================] - 11s - loss: 0.0610 - acc: 0.9836 - val_loss: 0.7653 - val_acc: 0.8867\n",
      "Epoch 83/100\n",
      "195/195 [==============================] - 10s - loss: 0.0638 - acc: 0.9825 - val_loss: 0.8647 - val_acc: 0.8930\n",
      "Epoch 84/100\n",
      "195/195 [==============================] - 10s - loss: 0.0678 - acc: 0.9818 - val_loss: 0.6797 - val_acc: 0.8812\n",
      "Epoch 85/100\n",
      "195/195 [==============================] - 11s - loss: 0.0677 - acc: 0.9828 - val_loss: 0.5567 - val_acc: 0.8766\n",
      "Epoch 86/100\n",
      "195/195 [==============================] - 10s - loss: 0.0724 - acc: 0.9819 - val_loss: 0.7608 - val_acc: 0.8899\n",
      "Epoch 87/100\n",
      "195/195 [==============================] - 10s - loss: 0.0653 - acc: 0.9827 - val_loss: 0.8088 - val_acc: 0.8929\n",
      "Epoch 88/100\n",
      "195/195 [==============================] - 10s - loss: 0.0676 - acc: 0.9829 - val_loss: 0.8454 - val_acc: 0.8862\n",
      "Epoch 89/100\n",
      "195/195 [==============================] - 11s - loss: 0.0686 - acc: 0.9818 - val_loss: 0.7546 - val_acc: 0.8909\n",
      "Epoch 90/100\n",
      "195/195 [==============================] - 10s - loss: 0.0733 - acc: 0.9822 - val_loss: 0.8424 - val_acc: 0.8905\n",
      "Epoch 91/100\n",
      "195/195 [==============================] - 11s - loss: 0.0655 - acc: 0.9830 - val_loss: 0.7271 - val_acc: 0.8855\n",
      "Epoch 92/100\n",
      "195/195 [==============================] - 11s - loss: 0.0677 - acc: 0.9825 - val_loss: 0.5669 - val_acc: 0.8853\n",
      "Epoch 93/100\n",
      "195/195 [==============================] - 11s - loss: 0.0608 - acc: 0.9843 - val_loss: 0.6216 - val_acc: 0.8845\n",
      "Epoch 94/100\n",
      "195/195 [==============================] - 11s - loss: 0.0692 - acc: 0.9821 - val_loss: 0.6956 - val_acc: 0.8835\n",
      "Epoch 95/100\n",
      "195/195 [==============================] - 10s - loss: 0.0686 - acc: 0.9821 - val_loss: 0.8316 - val_acc: 0.8907\n",
      "Epoch 96/100\n",
      "195/195 [==============================] - 10s - loss: 0.0702 - acc: 0.9824 - val_loss: 0.7418 - val_acc: 0.8904\n",
      "Epoch 97/100\n",
      "195/195 [==============================] - 10s - loss: 0.0684 - acc: 0.9830 - val_loss: 0.6174 - val_acc: 0.8844\n",
      "Epoch 98/100\n",
      "195/195 [==============================] - 11s - loss: 0.0652 - acc: 0.9837 - val_loss: 0.7586 - val_acc: 0.8927\n",
      "Epoch 99/100\n",
      "195/195 [==============================] - 11s - loss: 0.0661 - acc: 0.9828 - val_loss: 0.7556 - val_acc: 0.8850\n",
      "Epoch 100/100\n",
      "195/195 [==============================] - 11s - loss: 0.0732 - acc: 0.9818 - val_loss: 0.5831 - val_acc: 0.8930\n"
     ]
    }
   ],
   "source": [
    "h = model3.fit_generator(generator=gen, steps_per_epoch=50000//batch_size, epochs=nb_epoch, validation_data=(X_test, y_test))\n",
    "model3.save('CIFAR10_model_with_data_augmentation_dual_GPU.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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umTcGsEJz/gNLGJcWy9emZbDtQCXbCioYnhTNHeeNIyK045gWYwxrc8p4afU+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\nsNbMKEzB6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raqVrYfa6UkI5Fl5cWDju24HDdP3TE96nMmkxrU/8ipcpKd3HuaNgOUhiq5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19410df4630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acc_loss(h, nb_epoch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可以看到，第二个100代训练的提升几乎是微乎其微的。模型基本已经收敛。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy = 99.44 %     loss = 0.028977\n",
      "Testing Accuracy = 89.30 %    loss = 0.583130\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model3.evaluate(X_train, y_train, verbose=0)\n",
    "print(\"Training Accuracy = %.2f %%     loss = %f\" % (accuracy * 100, loss))\n",
    "loss, accuracy = model3.evaluate(X_test, y_test, verbose=0)\n",
    "print(\"Testing Accuracy = %.2f %%    loss = %f\" % (accuracy * 100, loss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S1sPFYlzLNTig52WT0GVMTertpFJak9Sd4m2UNOJdFCu8PcbHtY5OzsG3dDrJ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\nL0+yOjqq7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19421eb8a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "rand_id = np.random.choice(range(10000), size=10)\n",
    "X_pred = np.array([X_test[i] for i in rand_id])\n",
    "y_true = [y_test[i] for i in rand_id]\n",
    "y_true = np.argmax(y_true, axis=1)\n",
    "y_true = [class_name[name] for name in y_true]\n",
    "y_pred = model3.predict(X_pred)\n",
    "y_pred = np.argmax(y_pred, axis=1)\n",
    "y_pred = [class_name[name] for name in y_pred]\n",
    "plt.figure(figsize=(15, 7))\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "    plt.imshow(X_pred[i].reshape(32, 32, 3), cmap='gray')\n",
    "    plt.title('True: %s \\n Pred: %s' % (y_true[i], y_pred[i]), size=15)\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可以看到，基本上是10张里面错1张的情况，符合89%准确率的预期。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model | Data Augmentation | Accuracy (Train) | Accracy (Test) | Epochs | Time\n",
    "----  | ----              | ----             | ----           | ----   | ---\n",
    "Conv (64, 64, 128, 128, 256, 256), FC(128) | No | 99% | 80% | 100  | 950s\n",
    "Conv (64, 64, 128, 128, 256, 256), FC(128) | Yes | 99% | 87% | 100 | 950s\n",
    "Conv (64, 64, 128, 128, 256, 256), FC(128), Dual GPU Training | Yes | 99% | 89% | 100 | 950s\n",
    "Conv (64, 64, 128, 128, 256, 256), FC(128), BN after each CONV | No | 98% | 80% | 100 | 1647s"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
